From Hollywood. It's time now Johnny dollar This is Ben Stacy partner. Oh hi Stacy. We'll be docking a day car in an hour or so now. Yeah, I know Don't forget. I'm gonna show you and Helen around the town. Okay. I'll be ready The board ship last night, huh? That's Stewart who fell overboard. That's putting it politely. Oh, what do you mean? I think he got pushed Tonight and every week day night Bob Bailey in the transcribed adventures of the man with the action-packed expense account America's fabulous freelance insurance investigator yours truly Johnny dollar From special investigator Johnny dollar location at sea on the southern Empress our route Cape Town to Dakar To the home office tri-eastern indemnity Associates assignment the star of Cape Town matter expense account concluded Item 1250 cents room service on two aspirins Right from the start this whole deal had been a series of headaches headache one Andrew Forbes international playboy an owner of the star of Cape Town insured for a hundred and fifty thousand bucks For's was tossing that stone around like it was a cheap toy. He wound up murdered the diamond missing Forbes's probable killer Julio B. Ock was in a Cape Town jail at the moment, but his confederate was still on the loose The Stuart who could have told me who the confederate was had been fished out of that drink dead last night Now we were going ashore at Dakar and whoever had that diamond could make it hard for me to find It could be Stacy the boy from the wide open spaces It could be Helen traveling companion to Agatha Forbes and I knew if it did turn out to be Helen it'd leave me kind of sick inside Worst of all it could be somebody I didn't even know about Hence the aspera Stacy had asked us to meet him at the gangway at 10 o'clock But he was nowhere in sight when I showed up pretty soon Helen and Agatha Forbes came along Hi Helen Forbes good morning, mr. Dollar Helen just told me about what happened last night somebody trying to seize her on deck Dreadful perhaps. I shouldn't have told you miss. No. No. I'm glad you did anything happened during the night Helen No, I kept my stateroom door lock. Well, here you are. Sorry. I'm late. I guess I over slept already already Say now I'm mighty glad to see you with us miss Forbes. I was hoping you'd come I won't be going with you. I'm going to stay aboard and rest.  Stacy? I assure you I don't feel like frisking around a bit See you when you get back Say no, I guess I put my foot in my mouth talking that way. Oh, well, I usually do anyway. It's sure big enough Well one thing I had to give Stacy credit for he was a good guy He threaded us through street after street in the native quarter of the car narrow passageways crowded with people in stalls Where native hawkers were peddling all sorts of merchandise This is fascinating, isn't it like I told you Helen day cars quite a place I love it. I wonder if it isn't about time. We started back to the ship. See just a minute Helen I think there's a shop around here. You'd be interested in silks perfume stuff like that. You interested Sure. Well, let me get my bearings a minute. You need more perfume Johnny doesn't bother you that I still wear this perfume Andy Forbes gave me if it does I won't yeah Yeah, the shop I had in mind is right down the street. Come on Stacy led us to the shop Helen began trying on dresses and robes this I didn't like If she had the diamond, this was a golden opportunity to pass it along There wasn't much I could do about it Then all of a sudden I realized that Stacy was nowhere in sight. I went outside and started along the street looking for Then I spotted a man following me a native in a vernoose So I ducked down the next alley to shake him, but he didn't shake Then I noticed that this was a blind alley the native was closing in and what he had in his hand looked strangely like a knife Suddenly a door beside me. Come on in daughter.  I Figured sooner or later. You'd start looking for me. So I just thought I'd make it easy for you Thanks stick around Hassan. I may need you very well Hassan's a pretty effective persuader dollar a jolly So you're the boy. I've been after Stacy correction.  I've been after dollar What are you talking about a diamond called the star of Cape Town? Let's have it. Oh look Don't give me that routine Fores was knifed by your buddy Julio B.  I'm the one now. Let's have it. I Don't have it Stacy, and I don't know where it is. Oh, you got a real sense of humor dollar So as Hassan, why don't you show him Hassan very well? Hey look this isn't gonna do you any good.   It's no good trying to snow me I got it all figured out just what have you got all figured out that isn't in her state room It isn't in yours. I searched them both again this morning before we came ashore. That's why it was late You're talking about Helen who else that's why I steered her to that shop to try on dresses Little lady who owns it is a friend of mine. She have found a diamond if Helen was carrying Helen Yeah, Helen, how do you know she had it process of elimination buddy? She was the only one with Forbes before Julio got to him I've been watching you and Helen like a hawk dollar. There's only one time She could have given you the stone that was during that tender little clinch on deck last night right under my nose right under What's the matter dollar Nothing Yeah, what Stacy had just said popped the whole deal into place suddenly right under my nose Right where it had been all the time I tell you what dollar you got just five minutes to tell me where that diamond is Hassan will be here with you and he's gonna start persuading again if you don't talk I Had to get back to the ship somehow That meant I'd have to do some fast-talking. I looked at his end I couldn't tell which was glittering more as black eyes of the knife He held against my throat one wrong word and I knew he'd start carving How's that what do you want um you come from the desert don't you why you're a long way from home Have a good homesick What do you mean? Oh, I was just thinking with half the money from that diamond You could buy yourself an oasis with all the trimmings do not worry Stacy will pay me well when we get the jewel What's it giving you for this job three goats and a new Bernouse dollar. Hey easy with that knife It's just that everybody who works for Stacy seems to wind up getting paid off the wrong way Wrong take holy obiac for one the guy is Stacy hired to get the diamond in the first place He's roosting in the Cape Town jail right now. You lie you can check on it Then there's the steward who sent Stacy's message to be a he got shoved overboard. You can check on that too But I'm sure a thing like that would never happen to you Go on dollar okay, I Know where the diamond is How do I know I can trust you you can come with me to get it? I don't know At least he was thinking about it and it's all I wanted his eyes got that far away look That's what I was waiting for I whipped my arm up and knocked his knife I've buried my fist in this mid-section. He checked. I've a rabbit punch finished him off I dove for the wall just as Stacy came charging in I took the gun out of his hand Stacy was rugged, but I finally made it I Got that the car police to put Stacy and Azan on ice patched up my face and headed back to the ship But there was something else hurting me a lot more than my face Helen Johnny yeah, Johnny you've been I was Face what happened? Skip it. I have to hand it to you Helen That was a real class a snow job. You pulled on me.  Okay? They're pretty fancy bottle solid base Or maybe not so solid In the base of the bottle Star kept I'll bet you a real surprise. Don't you Johnny? I didn't know it was there. I didn't have any idea I swear it she's telling the truth. Mr. Dara. Miss Forbes Wow Well, I guess then That gun looks sort of out of character I won't hesitate to use it if necessary.  Have you any idea what it means to see somebody drag the family name through the dirt Time after time with his wild escapades and have you any idea what those escapades cost your brother wasn't dead Terribly, we were pressed to the wall. Oh, why didn't you sell the diamond? We were bound by the will not to But arrangements were made with someone in New York what we could get for the diamond plus what we could collect from the insurance It would be enough. Oh nice little scheme But you didn't figure on who do you'll be trying for the diamond in Cape Town and killing your brother in the process No First I didn't know what to do But soon became clear that more than ever I had to go through with a plan My brother's creditors began to make trouble right after his murder The diamond, please Now look don't you get it Agatha? You're licked. What do you mean? You've done all this to protect the Forbes name. Of course you failed the story's out. No, it's known only to the two of you How are you gonna keep us from talking? I'll do whatever is necessary. Privis kill us Sorry, neither one's gonna work. Mr. Dollar. Do not force me trapped Agatha By the same thing that got you into all this the Forbes name Are you going to brand it with murder? No, I don't think you are I don't think you could I don't I don't know please mr. Dollar I'll take the gun Agatha Thanks. Oh I guess I I Had failed haven't I all the way Expense account item 13 375 dollars and 50 cents transportation and incidentals from Dakar home expense account total 1283 dollars and 60 cents. I Turned the diamond over to the authorities for safekeeping and Agatha Forbes to face charges of fraud Holy Obliac and Ben Stacy were indicted for the murder of Andrew Forbes remarks about Agatha Guess she did what she did because she thought the end justified the means which is one of the oldest sucker traps of them all about Helen Well now that she's no longer a suspect Could be I'm no longer building up to a big letdown with her At least it hasn't come yet, and I'm still waiting and the waiting is real pleasant yours truly Johnny dollar Now here's our star to tell you about next week's story Next week a yacht that wasn't there and a man who wasn't there they never were But that's where I found them join us. Won't you yours truly Johnny dollar Yours truly Johnny dollar starring Bob Bailey is transcribed in Hollywood It is written by Robert Reif and produced and directed by Jack Johnstone Heard in this week's cast with Jean Tatum Virginia Greg Harry Bartell Chester Stratton Marvin Miller and DJ Thompson Musical supervision by Amorigo Marino Be sure to join us on Monday night same time and station for another exciting story of yours truly Johnny dollar Roy Rowan speaking


really sharing. Thank you so much. Okay, thank you. Gonna start. Yeah, so today's session, we are going to look at the hypothesis testing comparing two samples dependent samples of dependent groups. By the end of the session, we should be able to know how to conduct a hypothesis testing for two related population or two samples. This comes from the same population because they will be related and the data that we're going to be using will be dependent or the variable that we're going to be using will be dependent on the other. Therefore, it means when you look at the statement given, you need to be able to identify whether the case that they gave you or the statement that they gave you, the variable of interest that they want you to test. Is it independent or is it dependent? If it's dependent, how do you identify that? Usually it's the before and the after situation that happens. So you will be able to see if they talk about before and after before you join the organization, after you have joined the organization. Before you get tested, after you do something and then you get retested, then you will have a case of before and after. And then we'll also do some activities. So just to recap, we know that we can do two samples, but one can be independent and we looked at the independent that is if you have two groups, male and female, primary and high school. So as long as you've got two groups today, we're going to be looking at related samples where it is the same from the same population, but we're looking at the before, the group before and the group after the treatment. So how do we do that? So for a related sample, like I've said, we will have to calculate the mean we have to test if the two populations are related and we're going to be using the mean because most of the tests that you are doing in this module, you always going to be testing for the mean. Then it means the sample that you have needs to be paired or needs to be matched. The measures are going to be repeated because you're going to do before and then you're going to also test again after. And we're going because of the before and after from the same group of sample, then we need to do different calculation to get one value or to get the difference of those paired values. The reason why we do the difference is also to eliminate any variations amongst the subjects. There are assumptions that also needs to be met. Both of the sample or the population has to be normally distributed. Or if they are not normally distributed, then the sample size has to be large. And when we talk about large, we're talking about a sample size of larger than big. In order for us to calculate the test statistic, which will help us in making the decision, there are a couple of things that we need to be aware of and we need to know how to calculate them. We first need to calculate the difference. And after we have the difference, we need to calculate the average of those differences. So we calculate the mean of the differences and that will give us the point estimate of the differences. Because we require the point estimate minus the mean value to calculate the test statistic. And then we also have to calculate the standard error, which is the sample standard deviation of the hypothesis B. So the standard error or the sample standard deviation will be calculated by taking the square root of your difference of your observation, not your observation, but your difference observation minus the mean squared. So it means you're going to go and take for every observation a difference, you're going to subtract that value from the mean. And then you're going to add all of them, then divide them back or take a square root of each value and divide by n minus one. And we're going to look at the example where n refers to the number of pairs that you have, which will be equivalent to your sample size. Okay. So to calculate the test statistic, we use this formula. T, because we are assuming that the standard deviation is not known for the population. So we're going to use the T test. And to calculate the test statistic, we use the difference mean, sample difference mean minus the population difference, divide by your standard error, which is your standard deviation divided by the square root of n. And remember, the standard deviation is calculated by the square root of your difference minus the difference mean squared divided by n minus one. And once we have the test statistic, we can also go and find the critical value and we know the critical value. We find it by using the T value with your level of significance or alpha and the degrees of freedom of n minus one. And when n is similar, it's your sample size. When making a decision at the end, after you have calculated your test statistic and you have your critical value, you then can make a decision for paired samples works exactly the same as with any other hypothesis. Because you will know in your hypothesis testing statement, you will always have the mean difference is greater than or it's less than or greater than or equal or less than or equal or equal. So you need to be able to identify whether you're doing a one tail test, which is a directional test, especially in the less than for your alternative. Or you're doing an upper tail or a one tail test, one directional way it is greater than or you're doing a two tail test, which is a non directional test where you will have two regions of rejection. Now the other thing that you also need to be mindful of is in the statement, there will be key words that you need to always remember. For a less than, they can use words like decrease or less or fewer, something like that.  For a two tail or non directional, they will use words like change, different, as long as they don't give you a direction of less or greater, then you know that that is a two tail test. And the original rejection will be based on the sign that you have on your alternative hypothesis and when you make your conclusion and your decision, you based that decision and the conclusion on the null hypothesis, not on the alternative, but on the null hypothesis and you will either reject or not reject the null hypothesis. So let's look at the next example. Assume you sent yourselves people to a customer service training workshop and you want to determine whether have the training made any difference in decreasing the number of complaints and you went ahead and you collected the following data. You have your salesperson, you collected information about the number of complaints that each salesperson received before they attended the training and then they went and they attended the training and then you took the, you took the number of complaints after they have attended the training and you calculate the difference between before and after whether you use two minus one or one minus two, it's up to you. So I use two minus one, so before minus, sorry, after minus before and I get my difference for each salesperson. I need to also be able to calculate the other measures that I will require, such as the mean, remember, the mean is the difference, the sum of all the difference values divided by how many they are. So I'll take the sum of all these differences, which is minus 21 and divide by how many they are. I just count one, two, three, four, five, they are five divided by five and that gives me minus 4.2. So that will be minus 21 divided by five, that gives me minus 4.2. And then I need to also calculate the standard deviation, which is the square root of the sum of your difference minus the mean squared divided by n minus one. So it means for every line, I'm going to say minus two minus minus 4.2 and then I'm going to square the answer and I do for all of them and then add them all up. So I'll get it out there and then I will say five minus one, which is four. So I'll take the total that I got there. I'll take the asterisk total. So that total divided by four, take the square root of it and the answer that I will get will be 5.67. You don't have to know how to calculate this because I don't think they will expect you to do any calculations. They will give you the values. So I'm just showing I'm just demonstrating it how we got to the 5.66. So we have the mean of minus 4.2 and then we have the standard deviation of 5.67. Now we're going to first state the null hypothesis and the alternative always with the hypothesis. Our mean will be always equals to zero as we stated. So the population mean will always be, will be zero. So the mean difference between population one and population two will be zero because they are from the same population. So if we take population one population two because they are from the same population, they are going to give us zero. So looking at the information that we had previously, let's go there. They wanted to know if there is any of the decrease in the number of complaints. So already there we have our sign. So they want to check if there is a decrease or there are less complaints. So because of that then it means in our alternative hypothesis because we cannot put what the researcher wants to claim. We can only put it in the alternative. We cannot put it in the null hypothesis because in the null hypothesis it always contains an equal sign and a less than does not have an equal sign. So it goes in the alternative. So our alternatives will state that the mean difference is less than zero and we know that we have training and this is another additional information that they would have given you, which will give you the level of significance of zero comma one zero and we know that that will be our alpha and we have calculated the mean difference and when we go to the critical value we go and find the critical value. So our t alpha of zero comma one and the degrees of freedom of four is located at minus 1.533. So that is our critical value. We calculate our test statistic by substituting the value into the test statistic. Our difference, you remember the mean difference was minus the mean difference was minus 4.2 minus the population difference, which is always stated in the state hypothesis statement. It's equals to zero divided by your standard error, which is your standard deviation divided by the square root of n and your standard deviation, we calculated it previously, it was 5.67 divided by the square root of five and the answer that we get is minus 1.66 and from that we determine our region of rejection based on the critical value. So we know that our science said it's less than so we know that it is in the negative side, so we will put a negative on the critical value of 1.53 and we highlight where our region of rejection will be. So anything that falls in the yellow or whatever the color is that I'm seeing yellow or anything that falls in the yellow area we reject anything that falls in the white area we do not reject. So let's see where our 1 minus 1.66 falls it falls in the shaded area therefore we reject the null hypothesis and the decision will be to reject the null hypothesis and conclude that there is a significant difference in the number of complaints reasoning. So the before and the after shows a difference. This is another example of how you can do a hypothesis testing for two t-test, two sample of means on Excel. So if you remember our data that we have we had the five sales persons that before and after we took those data and we put it into Excel. In the Excel there is a data analysis tab but you need to activate it by using an Excel add in if you don't have that and we use that to calculate the test statistic and the output that you get is this and on that output it gives you the mean of it gives you your it gives you the measures for both the pre and the post so this will give you the measure of central tendency or location the mean and it gives you the measure of variation the variant and it also gives you the observation for each so you can see that the observation is five then it also goes and and say what the hypothesis mean was so this will be your your null hypothesis statement and your degrees of freedom remember it is n minus one and we did find that n minus one which is five minus one is equals to four you can see that on excel output it also gives you that then it goes and calculate the test statistic as you can see there is the test statistic now the thing that you need to be aware of is your test statistic will come back positive on excel but you need to be mindful of the fact that depending on your critical value as well you need to make sure that your uh test statistic it also takes the sign of the um sorry of the hypothesis the null hypothesis design that is allocated to the null hypothesis as well because if you look at this and you're going to use the uh test statistic as it is and you're going to assume that it's positive you are going to not reject the null hypothesis okay so uh when looking at the critical value for one tail test then we get the critical value for a one tail test and we get the critical value for a two tail test so since we were doing a one tail test as you can see there is our critical value and remember also the sign set less than so we're going to put a negative in front as well so now the other thing that you can also get which I haven't touched now is the p-value that is very important because most of the time in your exam or assignment they will ask you about the p-value so you also need to be mindful of that that for a one-sided tail the p-value is 0,0866 but for a two tail a um a p-value will be double so if I take if I take this value and I multiply that value by two I will get the two tail p-value because of the two regions of rejection it shares them if I'm given a two tailed value and I want to go to a one tail my arrow went way too ahead and I want to go there I need to divide by two because I need to split those two areas into two so that I can get the value for one so that is the thing that you need to be aware of and always be mindful of that if you are given the p-value for one and they are asking you to calculate the p-value for two tail you need to multiply by two if they have given you a two tail and they ask you to calculate one or give one p-value or a p-value for one tail test then you need to you will need to remember to divide by two okay so and that concludes me talking and now it's your turn to answer some of the questions are there any questions anything that you are missing from my explanation was it clear was it difficult or we can just dive in dive in into the activities okay so let's look at the activities question number one a matched pet t-test should be used when you are number one testing a two tailed hypothesis comparing means on a measurement from before and after a specific event comparing two variables which come from the same group comparing two means on a variable where data were drawn from the same population which one one two three or four can also write on the on the check if you do not want to speak out check the check for any responses is it option one two or three four think about what we just discussed today whatever I said a lot about dependent groups nowhere okay the answer would be option yeah my basis would be two because it's before and after yes it's option two number b two samples may be regarded as independent when so this is what we discussed last time I just want to see if you are able to differentiate what we learned today and what we learned last time dependent variable means there is a some relation between the two independent means there is no relation between the two there is nothing affects the other the other one doesn't affect the other when dressed with the other one the other thing affects the behavior of the other something like that so would it be option one that will be option one there also when you look at this think about everything that we discussed today right but let's see you need to be able to identify the key words in the question as well a sample of 70 people are tested on a test for assertiveness before and after a workshop in which they are given assertiveness training which of the following is most appropriate formula for comparing the mean assertiveness score before the training went the one after the training think about the formula we used today okay is it one two or three this one I can just also add the zero maybe probably it's that's the thing that is confusing you zero or mean d because they didn't because it's equals to zero anyway so the formula we used today would look somehow like this like this minus i'm gonna put the minus mean d and that's supposed to be a subscript not a big thing mean d so is it one two or three let's identify what is that that we need to look at the before and after so it will be option three so let's recap on this one so this is for when you have one sample it's hypothesis for one sample we did this I think in april already for one sample and this we did it I can't even remember when we did this one but this is for independent samples two groups and this is for dependent two groups as well right so you need to be able to differentiate which one is which and then remember with two groups it will be male and female something like that for one group it will just be one population and they will give you the mean of that and the standard deviation and then just calculate let's look at number three I think this will be the second last they will not be the second list to test the efficacy of a workshop aimed at improving people interpersonal skills a researcher applies a scale which rates the interpersonal skill of 20 participants before and after the participant in the workshop scores on use the rating scale before the general population have a mean of five and a standard deviation of 1.5 which one of the following is the most appropriate way to express the null hypothesis for this analysis what's very important is the before and after for sure I can tell you we do not represent any hypothesis testing using the point estimate because the point estimate is for the sample so therefore this one would not be correct and because we dealing with two groups we cannot represent like this this is for one group so that is not the right so we are left with two statements null hypothesis one says the mean is equals to the population mean of the other number four it says the mean is not equal to the population of the other now think about it in a high in a null hypothesis there is always an equal sign so so it will be equal less than or equal or greater than or equal will number four be one of the option yes that will be option option three is the correct answer because this one it null hypothesis can never have and never have a not equal a null hypothesis can never have a not equal the only signs that you can get in a null hypothesis is equal less than or equal or greater than or equal so when you see signs like less than not equal or greater than in a null hypothesis you must know that that is not the correct way okay let's go to question four which is our last question and then we're going to call it a day a researcher wants to test the following hypothesis the null hypothesis states that the mean is equals to the population mean one is equal to the population mean two and the alternative states that the mean population one is not equal to the mean population two if i can ask before i read the entire statement by looking at this sign is this a directional or a non-directional it would be non-directional it is a non-directional test okay on the basis of the data provided the output from a computer program indicates that a t value of t is equals to 1.72 was found so like i said previously they will not expect you to calculate the value of t they will most likely give it to you they will not expect you to go and find the p value they will most likely give it to you so on the basis of the data provided the output from a computer program indicates that a t value of t is equals to 1.72 was found with the p value for a two-tail test was given as 0.056 a two-tail test so that is very important to also remember because we're doing a non-directional two-tail test there because of that equal not equal sign and we have our p value what should the researcher do to evaluate the results at a level of significance of alpha of 0.05 number one this is the thing that this is most likely the checking if you understand how to use the p value so let's see what should the researcher do to evaluate the results at the level of significance of 0.05 number one he says divide the p value by two before comparing it with the p value number two multiply the p value by two before comparing it with the level of significance alpha number three says divide alpha by two before comparing p to z uh level of significance and number four says compare the p value as given now and you need to understand with that question is are you able to from the information given are you able to make a decision definitely yes you are able to make a decision without doing any calculation without doing anything by just comparing the value of p value and that the reason i now i'm giving you that the reason why i'm saying that is because the statement says what should the researcher do to evaluate the results at the level of significance it means what does the researcher have to do to make a decision and how do we make a decision because we know that this um our hypothesis testing our alternative says it's a two-tailed non-directional test therefore it means the p value if it's given to us uh and the p value is a two-tailed p value then there's nothing we need to do we just need to compare the p value and the level of significance and make a decision why cause the room for decision using p value says if the p value is less than or equals to alpha we reject if the p value is less than alpha not less than or equals to we reject the non high hypothesis that is the decision that is what the decision rule says now they've given you a two-tailed test they told us that the null hypothesis and the alternative hypothesis is a non-directional two-tailed test our two-tailed test they gave us the p value so what we can do with that is take the p value given the p value as given and compare it with that so we're going to take our p value of zero comma zero five six and compare it to our level of significance of zero comma zero five and determine if it's greater than so you know we can see that this p value is greater than or equals to or it's greater than uh the level the level of significance so therefore we do not reject but that's not what they're asking you they're asking you the step that I just said we just compare in the two values so it means option number four is the correct answer now here's the catch if if they would have given you and they would have said if they would have said the p value I'm gonna change all this statement let's remove only one piece of it if they would have said with the p value of one tail test given that the p value and I'm going to change and swap these values and they give you the value of zero comma zero three eight let's assume that that is the statement that they have given you when you think about it you go back to your alternative statement and state but my alternative statement says not equal therefore it is a non-directional which means it's a two tail test given that it's a two tail test then what the research needs to do to make a decision you will have to make a decision by looking at the p value so because this is a two tail test therefore your p value will be equals to remember I said to get a two tail test p value you're going to multiply the value of a one tail test to get the value of your p value and then you come and evaluate now which which option would that be it would have been option number two because option number two says you multiply your p value by two before comparing it to the level of alpha so we're going to do this step and then we come back to this step so if you are given a one tail test you're going to multiply that by two to get a two tail test p value and then evaluate or compare the two value the p value and the level of significance that is one scenario what if the other scenario was like this i'm going to give you another scenario i'm going to put everything back everything back but the scenario here changes to instead of using that sign let us assume that they said it is less than a day so if your alternative is less than therefore it is a directional test and it is a one tail test and they have given you all this information and they tell you that a two tail test it's p value it's equals to zero comma five six and the question is what should the researcher do to evaluate the results at a level of significance of so the researcher needs to make a decision does he have all the information required to make a decision definitely yes but is it sufficient to make a decision no because it's a two tail test p value in order for the researcher to compare the p value to the level of significance they need to find the p value finding the p value because it's a two tail they're going to take zero comma zero five six divide by two and this i explained uh with the example they uh on an excel thing so that will be zero comma zero two eight but this is just explaining that you will get the answer there for the one tail test because you want to make a decision for a one tail test and then come to that step and compare and make a decision but the appropriate answer would have been option number one so you just need to be very careful when you read the questions as well evaluate what you are given make sure that you understand the question look at the option and make sense of what is required to answer that question because you can see that with different scenarios you could have gotten either option one two or three as an answer all in the detail so with our question coming back was a two tail two directional the only thing that is required the statement in the non hypothesis compares to the two tail there then we just take the p value as is because it's a two tail already calculated given p value and the level of significance and make a conclusion compare the two and make a conclusion that means option four are there any questions other than if there are no questions so we can end the session any today we'll just end right there the absence of questions just to introduce again to if some of you are new and you don't know I do this from pambili analytics and our aim and mission is to close the gap in terms of literacies when we're looking at data numeracy statistics research especially the analytical skills and we offer a range of services that you can also benefit from more especially in terms of our interaction lessons or sessions we offer instructor led online training similar to how we're doing it right now at the moment we're running a special which I think it will be this month will be the end of this month and in June we revet back to our normal rate for specials which is the normal rate is 350 per hour and if you want access to the recording the notes you have to sign in as a member and join the channel as a member when you join the channel are very important before you sign up 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So you'll recall we kind of turned on payroll just to recap on the left-hand side, you have to buy payroll if you're gonna process payroll in QuickBooks. Your other option is to hire a third-party payroll provider, have them do all the kind of tedious stuff on payroll and you just enter into the system what's needed for the financial statements and to do the bank reconciliations. Otherwise, if you're doing it within QuickBooks, then you're gonna be processing the payroll within here and then there's some other settings up top that are up in the cog and then we can go to the payroll settings. So let's just go through these payroll settings. One of them is gonna be that mapping kind of issue that we talked about and we'll get down to that down below. Okay, so you got your general item. So you've got your general information up top. So this is the info you used when you applied for the EIN. So if you're doing payroll from an IRS perspective, you need another number that's an employee identification number. That's a number that's not just necessary for doing payroll per se or you might not want it just because you have employees. You might want it even if you're a sole proprietor, you being the only person doing work in the business so that you can give that number to others possibly if they need it for 1099 reporting purposes instead of giving your social security number. Then of course, the company type down here is gonna be important, sole proprietor versus other formats so that they can process the proper payroll. It should populate you would think automatically there because we entered that information in the company settings when we set up the general company settings.


This is a crucially important skill because lots of people tell you to go out and touch But if you go up like if it was my I know Damien if I see Damien at a bar. I'm like, hey, how you doing man? How you doing man? And he's like a Body build a boxer build. It doesn't even worry him if I go up and do that to Rebecca. Hey, how you doing buddy? How you doing buddy? She doesn't like it so much and I was probably as aggressive with Damien fantastic shoulders, man As I was with Rebecca So yeah, you touch women very very delicately again I always think of patting a baby duck and once you learn that skill you can keep your hand on them for ages and ages and ages I'm jumping ahead here, but one of my favorite quotes About succeeding at dating comes from a great 80s philosopher by the name of Mike Tyson And he looked into the camera. I'm not gonna do a Mike Tyson imitation and said boxing It's not about strength speed and skill. He says boxing is all about spirituality And it hit me. I'm like what and his claim And I'm not gonna argue with him was that he won the fight in the stardown He never lost a stardown till later in his career. And if you look at the greatest boxer of all time Muhammad Ali I mean he's you know one of my spiritual people. I know of his famous.  That's probably gonna be very good or try and Then you drop your eyes Down to her lips for about five seconds and then you move back up to her eyes Did you all get that so eyes? 10 seconds No talking slowly down to her lips and When you're doing this don't think man.  She's so beautiful and there'll be some sort of spiritual energy connection that will happen and the girl will feel that and If you've done that can you just explain to the guys? What a woman feels in that situation if a guy does it correctly?  Please It'll get you more sex. I promise Now how uncomfortable does that feel?  It's not an absolute guaranteed certainty But you know, it's pretty likely that you're doing well because it's just not comfortable You don't want to do this with a girl that you're not intimate with I am I did some Salsa at one stage and there's some attractive girls there But I had to interlock fingers with girls.  I was an ashram in India and I met these girls and they taught me one of the simplest lessons of my life But one the most powerful and Burya Rebecca I'm gonna teach you how to hug So actually might turn you around Hopefully you're still here hug a girl and just hold her gently hand a small of the back and Breathe when she breathes you can tell her this if you want just breathe in and out at the same time I don't know you like we're both starting to relax doing this just after a few minutes and In the seduction community. Thanks. There's all these incredible ways to get a girl into bed.  It saves you so much time How many of you have been on like four or five dates with a girl and then it just went nowhere How much time does that take how many of you know that on the first two dates with that girl She was probably very keen to sleep with you and she decided before the date that she was gonna sleep with you But you just didn't take any action So she might have been attracted to you to attracted to you initially it saves so much time in your life So let a girl know that you have a sexual interest in her by the three methods. I've shown you and see what happens Next time you're in a bar and you're having a good conversation with a girl Five ten minutes in try what I just showed you look her in the eyes hold eye contact Drop down to the lips and look back up to the eyes. Try that and see what happens I've done that many times. I've done nothing else I've you know, I've touched the girl in the arm and I've done that thing and often times the girl starts trying to initiate Sex herself, which is wonderful And if you guys have a date in the next few weeks Swear to God you're gonna practice it within the first half hour as soon as the conversation gets good Practice that and see what happens. This is really really powerful Remember what the great philosopher Mike Tyson said what would I what quoting Mike Tyson? Getting better with women meeting them approaching them dating them Sleeping with them so that they remember you for the rest of their lives. It's all about spirituality. It's about an exchange of energy I Really love to pick on English-speaking white men a lot Because we're really pitiful Particularly the really smart ones Some of the best lessons I've had in my life were guys from with from guys from Brazil or France I've traveled with guys and met guys Staying in backpackers who literally will sleep with more beautiful interesting intelligent, you know American girls Australian girls Canadian girls In their three months summer vacation a lot of our guys do in their lifetime. Can anyone tell me why this is? Don't care. Sorry They move things forward You know many of the Brazilian men I've hung out with their philosophy with women is I have a penis Therefore women will be attracted to me Do any of you like that philosophy? Do any of you have it? You wouldn't be here if you did The philosophy of most English-speaking men particularly the very intelligent ones is I need to do really well at school I need to get into engineering medicine commerce or something at a good school I need to study really hard and be one of the top graduates so I can get a great First-up job. I need to get work experience a good company needs to do postgraduate studies while I work and essentially Work 50 hours a week and then go do part-time study another 20 or 30 hours a week. I need to leverage myself the hill to get A nice car Place a nice postcode.  Yeah? Yeah, she came like a motherfucker, man.  The second thing is we don't let a girl know that we want to sleep with her and the third Oh my god, is my phone off and the third thing we do is we don't lead. So I'm Just want to have this for the camera I'm standing over here with my buddy


So our first speaker then is Chow Deana Minor and Chow is a Kenyan Digital Heritage Specialist and Digital Humanities Scholar working at the intersection of culture and technology. HUBBOK focuses on the application of technology in the preservation, engagement and dissemination of African heritage. Chow is founder of African Digital Heritage and a co-founder of the Museum of British Colonialism and a co-founder of the Open Restitution Africa project. Chow is also a recipient of the Google Anita Borg Scholarship for Women in Technology and we're absolutely delighted and honoured that she's joining us today. Chow, I'd invite you to turn your camera on, unmute yourself and I shall hand over to you. Thank you very much for the wonderful introduction. My name is Chow Deana Minor as you've heard. I am joining you from Nairobi, Kenya. I am a Black woman. I have braids and I'm wearing a very colourful top which is brown and black because valet stones are my favourite colour. And it's wonderful to be here today. I'm just going to go ahead and share my screen. If at all you have any problems viewing it, do let me know kindly. Today I'm going to be talking about digital infrastructure and how it relates both to digital innovation in an inclusive and community-centred way. The material that I will be sharing is mostly rooted on several projects that I have done here in Kenya and in the wider kind of African cultural landscape as a digital heritage specialist, as a historian and someone who's passionate about cultural representation, particularly in the digital age. I'm going to start off with this quote by Ikiyos that says that fighting for the future has always been an act of persistence of the desire to live. Now I start my keynote lecture with this because I do believe strongly that the work of digital technologies, particularly in relation to cultural heritage, to history and memory work, is not just about rooting ourselves in an understanding of the past, but actually allowing or envisioning ways in which this digital technology can itself be a path towards alternative futures and new imaginations of what our future could look like. In 2019, 2018, I founded African digital heritage, which is a non-profit organization that looks to provide critical holistic and knowledge-based approaches to digital heritage in Africa. Now the inspiration for this was coming from the point of view of looking at the ways in which digital heritage is rapidly changing cultural engagement, particularly outside kind of dominant forms of colonial representation, access to physical infrastructure, such as archives and museums being very inequitable, but also looking at the ways in which younger generations of Africans, of Kenyans, are pushing for models and engagement and frameworks of historical representation that suit them and that are more defined and essentially designed to cater for their humanity instead of the lack thereof. Now the numbers, by any chance, are quite astounding and intimidating. By 2050, two in every five children born will be born in Africa and by 2100, half of the world's youth will be African. Now what does this mean when kind of cultural representations, historical representations are not equitable in a digital age and we are expecting kind of digital technologies and the internet to be our primary source of information? So African information really steps in to kind of understand how are we going to make this cultural data accessible? How are we going to make it equitable? How are we going to make it sustainable? And finally, how are we going to make it cater for the audience? Not just in form of access, but also in form of challenging misrepresentations and bias that have existed within history. Our core forms of work are digitization, research, innovation and capacity building. And some of the major projects that we've worked on inform the work that I'm going to speak about today are a digital museum, no physical location and it's a volunteer run initiative called the Museum of British Colonialism, a project around mapping restitution data across the African continent, how many objects have come back? How many requests have been initiated? What are the responses to this request? And what are the cultural shifts in terms of data access? How is that affecting restitution and return of cultural heritage? And finally kind of major projects, which is one of my earliest projects to document rail infrastructure or the history of rail infrastructure here in Kenya. Now, through this kind of very wide rating projects, I have had both the privilege and the opportunity to really understand what digital innovation could do for cultural heritage, but also kind of what are the challenges and critical questions that we need to be thinking of. The first thing, rather the first thing I will look at is memory. If therefore we are going to sing, then we must sing quietly. This is a quote from the former attorney general of Kenya in the 1950s in a letter where he notes that human rights are being breached in the struggle for independence in Kenya, yet we must sing quietly. So he's talking about particularly the period of 1952 to 1960 in which the colonial government set up wide scale network of camps across Kenya to detain people who were suspected of being part of the Mao Mao freedom movement, which was an uprising calling for the return of freedom and land here in Kenya. Now, what happens after Kenya gains independence is that a lot of the information about these camps on the left, you can see kind of where they're located, sort of banishes out of national memory. I say banish, but banish is kind of abstracting the fact that the erasure of this history was very deliberate both on the part of the colonial government and the newly independent Kenyan government. Within the scale of detention camps, there were almost 800 villages set up in central Kenya, one million women and children detained and roughly kind of 150,000 people in detention camps, so outside the concentrated villages. As a Kenyan who was grown up in Kenya and who has experienced a public education system, it was very shocking for me to come across this information much later in my late 20s. What is more interesting is that these structures of detention, the archives surrounding the spirit of time are not accessible here in Nairobi and are very hard to access in the UK themselves. And so we began kind of challenging ourselves to imagine ways of curating and preserving memory and disseminating this history through digital technologies. Through this, we have begun creating digital reconstructions in 2D and 3D of how this detention camps looked or how the villages looked. Now, this process is not just about making information accessible. You see the colonial imagination says that we should be grateful for the knowledge that we have and we should not challenge it and we should not go beyond and ask questions. In this project, we are going to the communities we are traveling to the sites of these detention camps and we're interviewing veterans and people who experienced this site's first hand to help us imagine what they look like. Now, remember that we have very limited access to archives and we have very limited access to kind of the visual representation of what this site would have looked like. And so we are creating these digital reconstructions to enable canyons to, one, understand that this is very much a part of Kenyan history, two, be able to have a visual narrative and visual element to this particular period. And three, to be able to use this data, which often come with licensing and copyright restrictions in film, in education, in gaming and the like. These are examples of 3D reconstructions of concentrated villages in central Kenya, many of which were either brought down or burnt down or destroyed when Kenyan came independence. This is a trail surrounding the village and you can see kind of the spikes that surround it. This is another visual representation of what the detention villages look like. Here you can see the progression from archive to visual imagery or 3D reconstruction. And we also see this as a form of learning to enable audiences, students who are working in different disciplines to be able to take part in this kind of virtual reconstruction. What's interesting about this work is that I think it really shows that memory does not exist because archives and museums and libraries exist. Memory exists because memory is embodied in human beings. It is persistent, it is living. And therefore the work of archives and galleries and museums is not to constrain memory, but to enable memory to live, to be accessible, to grow into something else, to be challenged and to be useful to different populations and different generations. What you see here on the left is a picture of one of the detention camps in central Kenya when it was still in use. And on the right you can see some of its structures which exist today because the detention camp was turned into a school, but some of the structures exist. And so we're kind of breaking down the linearity of time to show that memory work, digital work, innovation is really about growing connections. It's really about understanding how spaces and memory and history and archives have evolved. For this project which I have talked about very, very briefly, kind of raises questions for me around digital infrastructure. When we think of digital infrastructure, a lot of the times we think of the server, the software, the content management system, the Excel sheet, the laptop. But I want to challenge us in it and I'd like to challenge myself as well to think about digital infrastructure being more than the technology, more than the equipment, and that this practice of building sustainable digital futures and sustainable digital projects is also about digital thinking and digital practice and digital futures. And these questions all come into play when we decide to start a digitization project. Me, digital infrastructure also embodies kind of four main things. Viewing communities as part of infrastructure. Viewing knowledge systems and indigenous knowledge as part of digital infrastructure. We have the tech and we kind of have the policies and the resources that are allocated to support digital infrastructure. And this is informed largely by work that I have done, but also kind of the gaps that I am witnessing when I work with museums, when I work with libraries around how we envision the place of digital technology for our societies, for our audiences, for our students, researchers, etc. And some of the questions that I feel we should be asking ourselves within this kind of practice of digital thinking is why do we digitize really? What is the intention of all the tons of data and gigabytes that we are generating? Who are we digitizing for? How do they access it? How do they know that this material is online? How do they use it? How do they challenge it? How do they generate conversation between themselves, but also between collections? Who are we digitizing for? And finally, how can digitization, digital work, digital practice really enhance our understanding of the path, but also give us the opportunity to challenge and to imagine kind of digital thinking as a freedom to look at the past in several ways. And of course, at this point in time, it's also important to note that digital collections also bear with them the same complexities, nuances, biases and inequalities that physical collections have. And so this is also a vital part of innovating for communities or rather I would say innovating with communities to understand what it takes. So this is a project that we did here in Nairobi and we're still currently kind of digitizing in a project to digitize over 100,000 archival records from one of Kenya's oldest libraries. And you can see in the video, we kind of looked at what does this work take? And sometimes I think we also need to be transparent about the resource, both the mental resource, the infrastructural resource, the social resources, or digital work. You know, how many rows of twine, how many face mats, how many workouts, how many kilometers of travel, how many gloves, how many interns, how many tribals. And all this is I think the very beautiful way of seeing that the infrastructure of digital work is not just the technology, it's the time, it's the capacity, it's the items that supplement the work that we're doing. What does it take to sustain this work, particularly from an innovation standpoint? One of the things that I hear often when I work with museums and libraries is that there is a fear that time is running out, you know, that somehow, somewhere, if as an institution, we don't digitize everything now, then we will forever be left behind and condemned kind of to the dark corridors of history. But I like to take this kind of fear and also challenge myself as well. Think of digitization and digital work as more about quantity and quality, not so much about speed around asking, it's about asking questions, asking the right questions, about having a freedom to fail and get something wrong, to iterate, to improve and to innovate and to collaborate across disciplines, across teams, across departments. We have just launched a podcast where I will share the link that looks at this kind of core questions around digitization and what we need to be thinking about, as well as how do we make this ethical, how do we make this equitable, and how do we make this sustainable for future perspectives. And so I do see innovation as an opportunity to challenge, to dialogue, to reframe and to represent. And in essence, from a practical infrastructural standpoint, I think there's a great opportunity to reevaluate metadata and cataloging systems, specifically for collections from the colonial period, to look at licensing and copyright and how does this fit into indigenous knowledge systems in which people own material collectively. Individual ownership is not something that you can assign to a particular object, to look at archiving and storage practices and funding and kind of regulations for that. And lastly, the curatorial and dissemination decisions that go into digital work I think are very crucial parts. Now I know I have probably squeezed a lot into this short presentation and I hope to have the opportunity to handle and answer more questions in future. I will share the links to all the work that I have mentioned. But as I summarize, I think it's important to say that the kind of ways in which we prioritize infrastructure, we prioritize the technology can be very dangerous. I recently came across a project in which they were digitizing material from the colonial, I think the early 1900s and the collections had a lot of derogatory, racially charged terms. And the dynamic was should we digitize this material and just put it online as it is, or should we redact kind of the material that is derogatory and mention this to our audiences. And I think in this kind of dilemma, it really shows that our curatorial, our humanness, our humanity is very essential to digital work that we are still making decisions that, as I said, the data that we are collecting and we are generating and innovating is not just about coalescing or mobilizing certain views of the past, but it's very integral to building futures. And therefore we have a responsibility both to our ancestors and to our peers and to our descendants to be making decisions around digitization that are holistic and that are centered on community-based human, humanity's kind of model. So thank you very much. I think I will end there and I look forward to the next presentation as well as the questions. Chow, thank you so much for such a rich, fascinating presentation. We already have questions coming in, which as I mentioned, we'll turn to after the second presentation. But anyone listening at home or wherever you are, do please keep the questions coming. I can see how many of you were also found that enormously thought-provoking and excellent presentation. So now we turn to our second speaker. Una Murphy is a lecturer of Arts Management at the Institute for Creative and Cultural Entrepreneurship at Goldsmiths University of London. Una's work helps museums, arts organizations, and cultural businesses to take a more efficient, effective, and creative approach to management with a particular focus on digital culture and capacity building. We're delighted that Una is joining us today to share her expertise as well and honored to have you with us. Una, I shall invite you to turn your camera on and unmute and I will hand over to you. So as Pip said, my name is Una Murphy and I am a lecturer at Goldsmiths University. I am a mid-30s white female. I'm currently wearing a bright green dress and some green and pink earrings as I embrace the heatwave that is London. In today's presentation, I want to do something slightly different. Normally, I talk about key studies that I've worked on, but I think looking at the program and knowing that Cha was speaking before me, what I really wanted to do was to take this kind of time and space to almost press pause and get us to think about some of those kind of wider digital culture narratives that we often have in the back of our heads but don't really have time to focus on because we're quite product-focused in developing, delivering new digital services and tools for our users. And so what I really want to focus on is asking this question, what does digital innovation really look like? Quite often, we think of digital innovation as flying cars and robots. And I think when it comes to digital innovation, we have these really interesting contradictory narratives, flying cars, robots, artificial intelligence, these kind of utopian visions. I like to call this the Jetsons view of digital innovation. And these utopian dreams serve a useful purpose in providing aspiration to what's possible with these technologies. But the utopian dream where work is carried out by machines as we enjoy a constant life of leisure feels somewhat of a stretch in reality to the work that we're doing. So in contrast to that utopian vision, we have this really interesting dystopian narrative, a narrative of disasters where freedom and creativity have completely vanished and we become screen-addicted zombies as shown in this illustration. So again, not an accurate representation, but there are elements of truth within that. If we extend that further, we look towards the Chinese government's social credit model, and this kind of totalitarian robot state where social credit and civic control are really intertwined in terms of technology. So it's quite a high level analysis of some of the digital culture narratives that exist within popular culture. And I think it's useful to frame our work in terms of digital innovation in this wider conversation about the power and impact. And I think as Chao said there, technology can be very dangerous. So picking up on her work and this rush to digitize, what I'm going to encourage you to do today in this presentation is to really press pause and to think about the impact that technology has on our users and on the data archives and collections that we have as institutions. So I'll give you those two extremes, one where we all quit work and live in cartoon flying cars and the other where we're screen-addicted zombies. I think the reality is likely to be more nuanced and exists somewhere between that utopian and dystopian fiction that prevails in popular culture. Indeed, if we look today, Neela utopian or dystopian narratives fully depict the impact of digital innovation as we experience it in our daily lives. So if we look through some examples of the more mundane uses of technology in our daily lives, as you can see in this image, this is a picture of Gmail, Google's email provider. And the picture shows how machine learning helps to filter spam from my inbox, but it also helps me to structure emails. So we're all familiar with this approach now. You're writing an email and Google or Outlook helps you to complete sentences. It's really an advanced form of spell check, but it's quite functional and useful. One of the problems I have with this technology is that it wants me to be polite and more professional in my tone, which I like to resist at all opportunities. Next, we can see here a picture from the travel app CityMapper. CityMapper is another great example. Google Maps also works. Both of these help me to travel in the most efficient way possible. They respond to live conditions and recalibrate my journey as I move. And my final example then is Google search. On the web, my search results are improved, meaning that I find what I'm looking for more quickly, but this quite often also limits any serendipitous opportunity for discovery, that joy of finding things in the archive. So whether that's finding an article about an unknown female scientist or a local hairdresser who doesn't pay to advertise online, we can begin to see here how digital innovation can curtail how we see and experience the world in our everyday lives. The thousands of photos I take on my phone, you can see an example of some of them on the screen here, are neatly tagged and categorized by computer vision technologies. So I can search for pictures of my birthday or look for the picture I took of her receipt during my last work trip. But as you can see in this search, the technology lacks cultural knowledge and infrastructure. So here I've searched for birthday and at the bottom of the screen you can see a picture of my granny and my great aunt. The computer has decided that this is a birthday because there's candles, but it's actually Christmas because you can have candles at more events than just birthdays. So this is just some top level insights into what digital innovation versus the mundanity of digital technology looks like in our everyday lives. We turn then to Alexa, as you can see in this gif of somebody shouting at a voice-operated system. Alexa and the extension Amazon employees are listening to users' conversations, but she and Alexa can't understand my Irish accent and so my arguments with her seem to outweigh any useful assistance it provides. Often we become frustrated and end up shouting at these technologies. What these examples show is that for me, cultural organizations sit below the line. So if we think about the examples I've shown you, what we see is a form of efficiency. Information is provided quickly, but efficiency often comes as a has a negative impact on issues of representation, creativity and accuracy. Digital technologies built in my life and my experience is more efficient, but the trade-off for an efficient life is less opportunity for discovery and also it's a life viewed through the prism of those that program the machine. So I think we can agree the landscape is complicated, but for me one way we can develop more accurate representative and creative approaches to digital innovation is by going underground to fall down the rabbit hole and the embrace the difficult journeys. So jumping onto my next analogy, we can talk about falling down the rabbit hole, but we can also start to think about digital innovation as an iceberg. So at this point in my presentation, this is where I would normally show you some really shiny examples of cool projects that people have done, but I'm not going to do that today. Instead I'm going to look below the line and I want us to start to focus and value the hard work that no one sees that doesn't get mentioned on the press relief. It's often slow, complicated, difficult and stressful, but it is the foundational work that creates inclusive, accessible, engaging, representative and diverse digital products, tools and services. And for me, these conversations should really happen at the initial concept stage. When we're thinking about applying for funding or seeking resources internally for a project, that's where we need to have the conversations about creativity, access and representation. So in this very brief talk, I want to introduce free tools to you that I think can help you to develop your work in a way that provides opportunities for creativity, access and representation. The free tools that I'm going to introduce are well documented and I'll ask that we'll share the links in the chat so that you can go and look at them yourselves. Each of these tools follows a similar format, but essentially it allows for the facilitation of questions around data ethics across the life cycle of product development. The first tool I want to introduce to you is the data ethics canvas, and this has been developed by the Open Data Institute. And what you can see here is a linear process that asks you to consider the implications of the data that you're collecting, the data that you're processing and the output data. And it frames it not simply in a legal framework, so what are the legal requirements and responsibilities, but it also asks you to think about the negative impacts on people, the positive impacts of people and wider conversations that I think are particularly important for cultural organizations and public service organizations such as libraries. So communicating your purpose, engaging with people, openness and transparency and sharing data with others. By working through this canvas, we can start to develop more robust approaches to using digital technologies in libraries and archives. What I think is really interesting here is that it takes a macro view of the impact of our work. It looks much beyond product development or project development and instead places our work into a wider context of the institution and its values and the wider impact the projects can have in society. The next tool I want to introduce is the consequence scanning. And so I should say all of these tools are tools that I have used in my own work to develop projects with museums and cultural organizations. The consequence scanning toolkit provides helpful prompts that allow you to facilitate conversations that situate the work that you're developing in a range of different contexts and environments, but also allows you to think about your users, both not and potential future users. And the final tool I want to introduce you to is one that I developed myself. My work over the last number of years has focused quite heavily on artificial intelligence. And working with museums and cultural organizations in the United Kingdom and America, we realized that while some interesting work was happening on a project by project basis in museums, there was no industry-wide accepted ethics policies, practices or processes to using artificial intelligence. And whilst we found consequence scanning and the data ethics canvas useful foundational tools, they lacked the nuance needed for publicly funded and social purpose organizations such as museums, libraries and archives. So we developed this free open access toolkit, which is available in English, German and Spanish. And in it, we have a series of case studies, a glossary and some theoretical questions around ethics and bias. But crucially, we have a series of free worksheets that are designed to encourage people to develop ethics and examine the social impact of projects from the initial concept stage. So ideally, we think that the AI capabilities frameworks worksheet should be completed at initial concept stage when an idea is being developed. And you can see on the left hand side, a series of prompts to help you to complete this framework. We then have our next worksheet, which is the AI ethics workflow. So again, on the right hand side, we have this worksheet that you can use in projecting team meetings to help you work through the prompt questions on the left hand side. So here you can see we're examining issues of data input. Where is the data coming from? What bias is already in that original data set? So that was something that Chao mentioned in her talk. A lot of collections data is problematic, quite often racist, and quite often issues of homophobia, class, disability. And so one of the challenges when we think about ethics in digital innovation is how do we mitigate bias? How do we document bias? And how do we use our platform as cultural and social purpose organizations to unpick bias and data that commercial organizations are less likely to do? And finally, we think that this work should be situated within stakeholders. So stakeholders may be service users, but they may be also people who are negatively impacted by data that is being processed that has historical biases. Engaging with stakeholders is a way to ensure that we deliver digital products and services in a manner that is creative, accessible and inclusive. So in conclusion, I would like you to think about how we value the process, how we engage stakeholders. I'm a big advocate that we should have less launches. We should take the money for those drinks receptions and we should use them for stakeholder meetings. And I'm a big advocate of pre-development meetings. How can you embed ethics at the initial concept rather than simply at project planning stages? Thank you very much. Inna, thank you so much. A second absolutely brilliant presentation and really helpful both of them with helpful prompts for where to go for more information and more things that we all ought to be considering, as well as fascinating insights into your own work. Chao, could I invite you to turn your camera on again and unmute and join us for the discussion. We've got about 20 minutes, just over 20 minutes. So we've got time to address some of these wonderful questions. If you haven't yet asked your question that you'd like to do, please put it in the Q&A. I can see there's a lot of activity in chat as well that Chao's been engaging into. A lot of love for the presentation and admiration for your work and the presentations there. But if we could start with the questions that have come in, as I say, to keep them coming if you'd like to. So question for Chao to start with. Is there a danger that funding bodies will dictate what will be digitized or when and if it will be digitized? And could you speak, if that's the case, could you speak to the implications of that? Absolutely. I would say one of the biggest challenges that we're facing is a lack of resources, especially financial resources to cater for this work. A lot of the work that we have and that is currently being done is funded externally, which is great because it does give us the opportunity to do this work. But at the same time, without policy, without government regulations, without support from public institutions here in Kenya or in the wider region, this work is very hard to do outside kind of external funding. And the danger that that comes with as well is the kind of restrictions or restrictions or implications on licensing and copyright. So you will find some funds that say you have to make all the material open access. And as we know, in as much as open access is a good thing in terms of conceptually giving everyone access, not everything should be made open access. Some material is sensitive. Some material belongs to the source communities that have never even had a chance to interact with this data in the first place before the world sees it. And so we have to kind of ask those questions around what it means to make all this material available without asking the right questions. So I would say yes, funding is a big challenge. External funding does come with different funders have different restrictions on how you should use the data. And it also gets tricky because a lot of this material is let's say national collections. Yeah, so I always bring this question up when the National Museum of Kenya works with Google Arts and Culture, who owns the data? Like what does it mean for national collections to be only accessible through kind of like private companies? And you know, the access question is great because all this material that was even basements or inaccessible physically is now online. But we're also talking about heritage community heritage. And so how do you navigate this very complex questions without stopping at and just assuming that all access is good access. So I'm not against access. I'm not against private partnerships and private public collaborations. I'm not against against funding. But I do feel that people, especially institutions and people working in institutions need to be equipped with the questions they should ask. So that the power dynamic of we want to digitize all your data here is the money is also met with the critical questions of well, what do you use this data for? How long will you keep it? Where will you store it? Do we still own the copyright? We need to be equipping people, especially museum workers, galleries and libraries, to ask and to feel that they have a right to ask these questions without shying away. Thank you very much. We've got a question for Una now. Also compliments on a great presentation and thanks. And it's a question about how transferable the museum AI toolkit is to non-museum contexts. So specifically the question that says for digital archives held in libraries, archives, universities and so on. Yeah. So I think it is very applicable to. So for me, I think the wider application is to data repositories of social purpose. So whilst the museums and AI network that I led looked at AI from two sides. So we looked at collections data, but we also looked at it from a management perspective. So ticket sales, social media, those type of things. The toolkit is very much designed to work for both sides of that. And so I think because we've designed it around collections, I think a lot of the questions that we raise and the framework that we've created is useful for libraries. And so I know that the Library of Congress cited it in one of their publications for developing machine learning strategy. So I think from my perspective, it would be very applicable. And I hope that it is. But I also really encourage anyone we're really keen to have more use cases of how it's been used. So if anyone does want to embed it in a project, please do reach out to me. It's free and open access. But I'm more than happy to get involved if people need somebody to come in and facilitate that conversation using the toolkit. We're really excited to keep getting new use cases. And it actually it was translated to German and Spanish this year because we had run a number of workshops using the toolkit for German museums. And we had a number of libraries there and a number of university collections that used it. And so we're kind of slowly building up our use cases off this toolkit. So I'd say very applicable, I hope. But if you'd like to use it and if you have any questions, just drop me an email. And I'm more than happy to kind of give you some advice on that. Sorry, Zima is playing up slightly. Thank you. And gosh, what an enormously generous offer. I suspect you're about to be inundated. And basically, as our chair also shared her email address in the chat. So thank you for your generosity, your collegiality. Tau, there's a question for you about whether you're also involving early career researchers in your projects. And perhaps, given the audience, the participants in this conference are also from the GLAM sector, Galleries, Libraries, Archives and Museums. Perhaps you could speak to early career people in those organizations as well as researchers. Absolutely. I do feel that ways of supporting this work are not just financial and also in terms of skills and time. All the interesting work, like the work around the documentation of the camps is volunteer work. So that's kind of the work that gets the most interest, but it's harder in terms of facilitating that work. So we do accept early career researchers. We accept interns. We accept people who want to use our platforms to share their work, to disseminate their materials. So yes, we do. And please get in touch. Thank you. Thank you very much. I'm really taken by how both of you are focused so much on people in communities in what is headlined as digital innovation in GLAM and really underlining that there is no digital without the people and the people are the ones who create it and implement it and have such a huge part to play in it. So it was really wonderful hearing you both talk about that, just the people in the technology. Una, could you speak to how people could help build up their ethics facilitation skills? I think that's an enormously rich area and people are clearly interested in this. Yeah, so I think it is a real challenge in terms of people's confidence in this area and being able to represent and facilitate diversity points can be quite challenging. I think particularly in the UK context we're kind of in this weird, woke kind of culture war situation at the minute. And I think that actually that is really kind of curteals our ability to have difficult questions in meetings. And so I think for me, I've been doing some training with the Open Dabba Institute over the last couple of months because whilst I developed this toolkit it was very much sector focused and I think it's really useful as professionals that we look at different disciplines and we look at different approaches to how ethics conversations are happening. So a really good example for me is Google a couple years ago fired one of their ethics researchers because she said that their approach wasn't ethical and she published a paper outlining issues with some of their data. And I think that for me is a really good starting point about how cultural organisations have a space where they can have these conversations because they're not commercially driven. But I actually think that in terms of ethics facilitation, libraries and archives actually have the potential to serve really important public conversations about ethics. And by documenting those conversations that we have, whether it's blog posts, whether it's sharing at community events, we can encourage members of the public to develop their digital literacy so that they can be more critical of how they use digital technologies and the data that they share in their everyday lives. Thank you, thank you very much. Tau, we have a question here for you which it's quite a big one. So how do we ensure that the biases that the questioner would say were inevitable, the inevitable biases held by GLAM professionals who are involved in acquisition, cataloging and so on are recorded? That's a good question. How do we ensure the biases that the professionals have, not the data? Well, that's a good question because it's not something that I think I haven't answered for. But I've later been thinking about kind of objects, biographies and data, that kind of the biography of the data sets, like the one image that you take, and yes, there's the kind of contextual material and cultural material or physical kind of attributes. But also how do we kind of embed the different biographies that this artifact or this image has been through or contains? I don't know, I do think that there's ways of kind of looking at this from opening up the collection standpoint. But it is also a lot of self-reflection work that I think you cannot necessarily, how do I say, turn it into like a clinical method. And therefore the work of self-reflection also has to be organic for it to be effective. And perhaps like something that come up with either as an institution or as a department and what is the responsibility around communicating that, but not just communicating, but also addressing how it has been. Now that you know that there's bias, how is it dealt with? I think I'll ask more questions, but yeah. And I think just to pick up on that, I think one of the things that I find really interesting in this kind of thinking about how we kind of, yeah, like object biographies and how we kind of document approaches to data creation metadata around objects and archives. One of the things that I always think is really interesting is we have to remember that while we think that the work we're doing now is correcting historical biases, the likelihood is in a hundred years people will look back at what we have done.  That was like so gendered or like culture and society changes. So I think as much as whilst I kind of in my work look a lot of bias and approaches to mitigating bias, I think it's really important reality check to remember that what we're doing is right for now. And whilst we might think that the people from a hundred years ago were awful and what they said and did and recorded, the likelihood is in a hundred years people will also think that everything we've did is awful. So I think there's a good reality check and we're mitigating biases so that the data we hold is more appropriate for now. But I think knowing that we're probably doing it wrong takes a bit of pressure and risk off and actually recognising that we're doing the best that we can with the information we currently have. And I think documenting that process so that when somebody comes to unpick our hard fought work in a hundred years, we've made that process a bit easier for them than the process that we're going through to try and mitigate biases now. Thank you. They're both really interesting and insightful approaches to a really difficult question. I think the answer of how do we record them is we don't know them all. But hooray, we are not at least we hope we are not the end point of history. We hope we're a point in a continuum. So other people may be able to come and clear up our mess after us. Chao, could I wrap two questions together? One is about the main challenges. So what are the main challenges with the African digital heritage work that you do and with your other work? In fact, and if you could possibly address that alongside this other question which I think chimes with it well, which is what kind of collaborations or partnerships are useful to your work? It seems to me that perhaps collaborations and partnerships might be part of the answer to the challenges. So in terms of challenges, I would say the biggest challenge we have is just financial resources. And my financial resources, I mean the money to pay people to do this work comfortably, the money to buy infrastructure, the money to have service space, the money to train. I don't necessarily think we have a big skills problem. I think the innovation and skills capacity, both technical skills, cultural, curatorial skills are great. But we don't have kind of the resource to enable people to focus on this effectively. And so someone will say, but I don't even have money to pay my staff to do the work they're supposed to do. How can I have money to think of digital technologies? And I think this is also a problem of thinking about innovation and this kind of digital space as the other thing. So we know when we're allocating funds for conservation and curation and marketing and engagement, we think of digital as the thing that's like the side piece and the extra thing. It's not a core part of how an institution sees itself. And so I think there's also challenges around the perception of what technology could do. One, that it's an additional thing, but also there's a challenge around feeling that digital technology is a threat, you know, that it's going to take away jobs. It's going to make people redundant. It's going to prevent audiences from coming to the museum because everything is online. So I think there's a big challenge around also demystifying and also rightfully acknowledging that while those challenges may be valid and may be real within the context of these institutions or these roles, that there are also ways of inviting people, organizations, communities that have not seen themselves as part of being the digital ecosystem into the space and asking them, what does your role look like with this technology in place, you know? So I would say the financial resource kind of the alienation of digital technology and alienating people and finally kind of the barrier in which people think that digital technology is not for me or it's too far for me to access even conceptually or mentally. In terms of collaboration and partnerships, I think the things that would be most helpful for us are one, collaborating with institutions that have possessed collections, particularly of African heritage and what we envision ourselves as also being a kind of a way to connect people to collections to help facilitate engagement, to ask questions around dissemination and so collaboration around collections, collections, engagement, collection, dissemination and dialogue, making collections accessible are very important to us. Collaboration around skills and capacity building. I think that's one of the big challenges that we have that there's a lot of need for capacity building and digital training and thinking and we're still quite small an organization and it's hard to kind of meet the demand for that resource and so I was so glad to see Una on the call because I've been such a big fan of the toolkit of the AI museum toolkit and now I get to actually see how it came into into being so what I would like to see is hopefully the development of such two kids that we can develop two kids on digital thinking, planning for digitization, digital ethics, I think would go a long way not just within the African heritage space but also globally. If we can be able to democratize and and demystify a lot of the skills, it would be very crucial as well. Thank you for such a very rich answer and wonderful to see admiration. I think mutual admiration for your work and certainly for me for both of yours. We've got so many wonderful questions that I'm afraid we're not going to have time to get to about audiences and about ground truth and data sets and all sorts but so with huge apologies to people whose questions we're not going to get to and thanks very much for all of them. They've been really stimulating and lovely to see this live engagement with with both of our speakers work. Can I just ask could I ask you for as a way of summing up we've had a specific question about advice that you might give to someone who wants to pursue a master's program in your field but perhaps while absolutely addressing the master's one other master's question could you also give a couple of words of advice to anyone who wants to take their first steps in the field professionally or or as part of a community as on a volunteer basis. So how do you get involved but if you could also speak to the master's question that would be excellent. Unesh I'm going to turn to you first. Okay so I'm not I'm not going to recommend the specific masters. What I would say is I'm a really big fan of recommending that students complete a master's part time because it provides you with time and space to basically milk the university for all its resources for two years and but it also gives you two years to utilize networks and contacts and but also to work part time and embed your skills. So it's not always possible but financially it can be helpful for some students because you can work part time ideally in the sector that you want to work in but it also means you have two years of access to resources and databases and guest lectures and all of those things and so I think that would be my recommendation of completing a master's. It doesn't really tell you what master's to do but I think it's a useful way of developing your skills quickly on how to get involved in things. I'm a big fan of digital networking. Chow is also very engaged in online communities. I did my research. I started my career in Belfast in Northern Ireland at a time when nobody was that interested in digital and I did a lot of networking with museums in London and New York Free Twitter and but I think also now in terms of development skills you've great crowd sourcing platforms like Zoo Universe so you can develop those kind of digital practical skills at the time and place at Sotu. Thank you very much. Chow, the same question to you. Well I don't know if I have much to add. I would say that I think the work of being innovative is also exposing yourself to the risk of failure and by failure I mean things not going your way. I don't mean that you have failed. I mean that something has taken a different turn from what you expected it to be and I think that both for masters and kind of community work, volunteer work, a lot of times things will go a different way but I think that is also kind of allowing yourself to innovate in the direction of what you find, whether it's community needs, whether it's within the history, whether it's the questions that you're asking yourself when you encounter the archives. I also think that sometimes it is good to empathize with the subject, with the material, with what you're doing and it kind of brings a more human nuance even in the digital space.


Adaptive cloud-based learning systems, ACLS, have been gaining popularity in recent years due to their ability to provide personalized learning experiences for students. In this paper, we explore the potential applications of ACLS in the Ukrainian higher education system. We discuss the design and implementation of ACLS by the Institute of Information Technologies and Learning Tools of National Academy of Educational Sciences of Ukraine and present the results of a survey conducted among 31 higher education institutions regarding their use of ACLS. Our findings suggest that ACLS has the potential to revolutionize the way education is delivered in Ukraine and that it can be used to create more engaging and effective learning environments. This article was authored by Yulia H Nosenko, Maya V Popol, and Maria P Shishkina.