recording and welcome back everyone on Moodle as well. So today we will be talking about statistical testing and I will do an example on microarrays. The thing that I wanted to discuss is not so much like very advanced linear models and things. Now I want to start very, very basic. So I first want to talk a little bit about experimental design and about some of the questions that you can answer when you use microarrays. Then I'm going to talk a little bit about the normalization of microarrays and then we are going to talk about probabilities and calculating more or less p-values using things like a student's t-test and other nonparametric tests. Of course, when we're talking about different statistical tests, we should also talk about the assumptions of each of these tests. So every statistical test that exists has a certain assumption underlying it. It can be that it assumes that your data is a normal distribution or that your data is drawn from a Poisson distribution. I want to talk a little bit about correlation as well and, of course, I want to talk about multiple testing, which is very important when you are analyzing microarrays because you're not just doing a single statistical test, but microarrays, they measure all genes in the genome. So you generally want to correct for the fact that you do 20,000 or 30,000 tests which are all more or less independent tests. And then I also want to tell you for today where you can get some free microarray data. So in case you want to work on some micro data at home and want to get a high impact publication using data that other people collected, then head to free microarray data or sources where you can get free microarray data are pretty important. And you can get some data here so that you can kind of work on it yourself. All right, so the first thing that I want to say about project planning is not something that I said, but it's something that Ronald Fisher said. So, and he said that to consult the statistician after an experiment is finished is often merely to ask him to conduct a post-mortem examination. He can perhaps say what the experiment died off. So the thing that he wanted to get across is that when you are planning to do a scientific experiment, you should consult the statistician directly before you start doing anything because statistics is fundamental in the analysis. And you need to kind of have an idea on what you are going to analyze, which effect sizes you're going to expect and how many individuals or how many samples you need to have before you can say that something is significantly changed or significantly different. And I see this a lot in my line of work that people with no proper or with no real understanding of statistics, they design an experiment and then they do the experiment. Sometimes the experiment takes like a couple of months to perform and then they do their data analysis. They don't really find anything and then they come to me and then they ask, can you help me? And at this point it is too late. I once had a partner who was working on apple trees and these trees take around 10 years to grow. And then in the end, I had to tell them that they should have planted five more trees. So that they were short five trees and that's why they couldn't prove anything. And of course this is very, very sad because a lot of money is invested, a lot of time is invested. And then to tell people after 10 years of working on a project that they should have planted five more trees is not a very happy message. So as a word of caution to you guys, if you start doing a project or planning a project and you are controlling how this project is designed, talk to as many people as you can, get input, not just from your professor or from people that you work with, also try to find someone who is knowledgeable in statistics. And because it is very important beforehand to make sure that you put enough samples in the ground or that you measure enough fish or that you order enough mice to do your experiment, because in the end it's really a shame if you finish your experiment and you end up realizing that you're five plants or five animals short. So some of the questions have for experimental design. For example, if we do microarray, so the questions that we can answer using microarrays are questions like which genes are differentially expressed between healthy tissue and disease tissue. And this is of course very important in cancer research where you want to compare things like lung cancer towards like the primary tissue, towards lung tissue and want to find the differences so that you can have some idea of which genes might be worth targeting. Had questions like which genes are expressed in which tissue? For example, we as a group, we work a lot on obesity. So of course the genes that we're interested in are the genes that are active in metabolically active tissue, right? So genes which are active in muscles or genes which are active in fat tissue. And we're not so much interested in genes which are active in your toenail or genes which are active in your spinal cord. Although they might play a role, a priori you would not know if they have and you want to know for your tissue of interest which genes are expressed and the genes that are not expressed probably don't play a role. So microarrays can help you figure out in which tissue which gene is expressed. One of the other things that microarrays can also help you is to kind of figure out what a drug does. So imagine that we're testing a new drug for lung cancer and then of course we want to see if giving the drug changes the expression of genes after we gave a drug or a certain treatment. And so these are like three main questions that we can answer when we use microarrays. So what is a microarray? So a microarray is actually a very simple thing. Have we have things like spotted microarrays so they can measure the expression level of more than 20,000 genes in a single experiment. And the way that they are is you have kind of a solid surface which in many cases is just a little piece of glass.  And these little pieces of DNA are designed to be complementary to the DNA of the target genes that you want to measure. So it's kind of a fishing expedition. So these little spots on the glass surface or on the solid surface are called probes or oligos. And what you do is you extract RNA from your target tissue. You do reverse transcriptase to go from RNA to DNA. And then you label your DNA using fluorescent dyes. So you have, for example, a green color on healthy tissue and a red color on disease tissue. And then what you do is you take your healthy tissue and your disease tissue and you mix them on the microarray. And then what happens is that the gene sequence will flow across the glass plate and it will bind a complementary strand when it finds one on the glass plate. So, hey, this is done by hydrogen bonding. So it's a reversible binding. But once they bind, they are kind of stuck. And of course, if there is more of one color binding here, you can see a difference in color, right? If a spot turns green, you know it was active in healthy tissue. If a spot turns red, you know it was active in disease tissue. So the color of the spot tells you something about how much of a certain gene or certain fragment of a gene was inside of your sample. So the way that it kind of works is here we see the microarray. So we have CDNA probes, we do amplification and we have a target, we have a sample target and we have a control target like this disease tissue and the healthy tissue. We extract RNA, we do reverse transcription and then we label with fluorescent dyes. We then mix both samples in equal quantities. We put them on the microarray. We use a laser to excite. Actually, we use two lasers, one from the red channel and one for the green channel. In the end, we end up with two pictures and here we see a microarray more or less scanned and if we zoom into a single microarray, what we see is we see all of these little dots. Yellow dots, of course, are dots where a gene was expressed equally in healthy tissue compared to disease tissue. Red dots like this one here are sequences which are highly expressed in the disease tissue but lowly expressed in the healthy tissue and of course the green dots are the opposite. So when we get microarray data or when you have done all of this scanning and hybridization and these kinds of things, generally what you get from either a company doing this for you or when you do it yourself, you then flatten this image information into a matrix and the matrix generally has samples, so the different samples that you used in the columns and all of the genes that you've measured are in the rows and then each of these little cells just contains the expression level of a gene. So that might mean 15,000 intensity for red divided by 2,000 intensity for the green channel and so it's just a big matrix and we have two factors. So we have gene names and we have sample names and of course we have sample annotation as well because we need to know if a certain tissue was healthy tissue or disease tissue and for the genes the same thing, we need to know for example where a certain gene is located and if there's any annotation or these kinds of things. But in the end, we're only dealing with the big matrix of data and this data is just the expression levels of the individual genes in the experiment. So there's a lot of things which can go wrong when you're doing microarray. So here we see a picture of a good quality microarray and we see very clear dots but sometimes this happens. So you look at your photo and you see that there's this kind of a scratch or a fiber which is on top of the microarray. So these things happen. So there's a lot of little things that can go wrong and of course we now just have to process the data and say well all of the dots which are underneath this thing or close to this thing, we're just going to ignore them and they are going to be turned into missing values. Besides that things which happen because they are little glass plates and it's very minuscule is things like this where we see that we have no data on a certain part and this is generally an air bubble on the microarray between the slide. So we have like no information on this microarray for these probes here. So all of these things we can kind of see when we look at the pictures and then we can transform this information and say well these data points are missing. Although the original measurement of course is zero intensity because this is a bubble, we can say no it's not zero intensity, it's just that there was a bubble. So it's a missing value. So something went wrong. There's also something called the edge effect which you can very clearly see here. So here you see a microarray, you see that there's kind of a green haze across the microarray which is not too bad but here at the top you see that there's something going wrong and this is the edge effect. So it's where the samples or the fluid that you put on of the microarray it hits the edge and it kind of doesn't really hybridize very well. You can still see the dots but of course because of this kind of overlaying like reddish haze on this edge you can't use the values at this point. There's something called background haze as well. You see a little bit of background haze here but here you very clearly see. So these are four different microarrays and you can clearly see that there's something really, really weird going on with this part of the microarray that there's like an intense green background color which can have various reasons and not only that but we see kind of a scratchy thingy here as well on the bottom where it seems that there's something which is on top of the microarray. And so all of these things we need to take into account and we need to more or less do quality control on the data that we get. So we look at the pictures and then based on that we make a decision is this a value that we can still use or is it a value that we just have to ignore and just say well it was missing. So unfortunately we don't have any information like that. So the common microarray workflow kind of looks like this. So we create our oligo arrays which is part of a bioinformaticians job. So a bioinformatician is responsible for selecting which genes we want to measure and which part of which gene. So we have to create these little oligo probes that we put on the array. Then generally a biologist will acquire samples so they will go fishing and take the fish and extract RNA from the fish. There's this DNA or this RNA to DNA reverse transcription step where we use a protein which transforms RNA into DNA. Then there's an optional PCR step. We do labeling using Psi3 which is the green color and Psi5 which is the red color. Then we do hybridization and scanning and hybridization and scanning gives us something called a TIFF file which is these image files that I showed you here. So these are just TIFF images and you get that from the scanning machine. Besides that there's something, there's the next step so going from these TIFF images we can go to a cell format and this is the standard format for storing microarray data. So if you go to these three microarray data sources which I will tell you about at the end of the lecture these generally store data in a cell format. So the cell format contains the intensity and the scanning but it also contains information of the probes that were there so that you can use things like correct for GC percentage of the probe or other effects that might occur. So what do we do then? Well we take these cell files, one for each sample or one for each microarray that we ran and then we extract the expression levels and this normally goes into a TXT file so a comma separated file or a tab separated file which is just a text based file where we see the intensity value. So this has more or less the structure that I showed you before where we have samples in the columns and we have the different genes or the different probe expression levels in the rows. And then the next steps that I wanna talk about is about the data normalization step which again results in a text file. Then we have a gene expression analysis step which generally results in a text file as well and then the data interpretation step so the gene expression analysis is looking or doing statistical testing to figure out which gene is different from disease tissue to healthy tissue and then the data interpretation step is clustering for example these things together to see if a certain pathway is involved or if there's something else going on like it doesn't have to be pathway but it could also be that for example a lot of microRNAs are up-regulated or a lot of proteins that have to do with cell cycle are down-regulated and that is the data interpretation step. But the thing that we will be focusing mostly today is data normalization. So when I get my data in this matrix format how can I now transform it so that we can do statistics on it and then after we've done statistics I think there are one or two slides about how you can interpret the data or the statistical results. So why do we normalize microarray data? Well the idea behind normalization is that you want to remove technical variation from noisy data because there are many different steps when you do one of these microarray analysis and every step introduces random errors. Right, the making of the microarray might include some air bubbles. When you do your DNA to RNA step it might be that for one sample you used a little bit more transcriptase. So it just has a better efficiency so there's more DNA in this sample compared to the other samples. So all of these things are things which increase the variability in our experiment but the assumption here is that these global changes across samples are due to kind of unwanted technical variability. So the source of it is not real biological variation but it is variation which comes in because some of the microarrays were scanned on Thursday when it was really warm outside and the rest of the microarrays were done on Friday and on Friday it was really cold and there is an influence of temperature. And so the fact that all of the microarrays on Tuesday are a little bit higher expressed than the ones on Friday has nothing to do with biological variation but has to do with the temperature in the scanning room at this point. And of course at each of these steps variation is introduced and we assume that all of this variation is kind of random that there's no, that it's not coupled to a single sample or to a single gene. No, this is just variation that comes in from the outside and we want to get rid of it. And so we remove these differences. However, we always have to keep in mind that by removing these global differences by kind of normalizing our data we might remove interesting biological-driven variation. And I think that during the rest of the presentation there will be an example where we are actually removing biologically interesting data from our data set. But in general, the assumption is is that normalization is good because it gets rid of all of these things which might have gone weird or are slightly different for each sample in our analysis. So there are three major ways of normalization or standardization. So the first one is relatively easy. It is called mean centering. So because every sample, the mean, so the average expression of each microarray is a little bit different. So I do microarray one and the average expression is 2,000 intensity units. The next microarray that I do is 2,100 intensity units. The next one that I do is like 1,915 intensity units. And so each of these microarrays is a slightly different average. So mean centering just means what do we do? Well, we take all of the old measurement values and then we subtract the overall mean from the microarray and then we just say this is the new intensity value. So we just take the old value, subtract the mean which is calculated across the whole microarray and then we have our new measurement value. So now of course the mean, after doing this, the mean of every microarray will be zero and they will all have the same mean because we just subtracted the average out of the whole data set. This is a relatively good technique because you get rid of like big variances in the mean. So things like having used a little bit more DNA for one sample compared to the other sample, this will be taken care of using mean centering. There's a different way of doing it and that is also correcting for the variance, right? Because it can be that some microarray or that one of the microarrays has a variance so the lowest expressed gene has an intensity of 100, the highest expressed gene has an intensity of 16,000. And then when we look at the next microarray, the mean is very similar or slightly different but now the range is completely different. So instead of going from 100 to 16,000, this one goes from 200 to 20,000. And so the standard deviation is something that we can also more or less harmonize across the arrays. So how do we do that? Well, we can use the standard score or it's called the student's T statistic. So what do we say? We take the old measurement, we subtract the mean, so we mean centering first and then after mean centering, we divide by the standard deviation of the whole group. So this now means that when we look at the new data, right? So the new data, when we calculate the mean of the new data, the mean is zero and the standard deviation is one because we divided out the standard deviation. So now we have data which is relatively clean. Why? Because every microarray has the same mean and every microarray has the same standard deviation. Another way of doing this is called quantile normalization and this is a kind of nonparametric method where it uses the rank of the individual kind of probes to harmonize the data into different quantiles. So instead of saying, well, no, we just divide out the standard deviation, this quantile normalization is a slightly more advanced method where it looks at the different quantiles and how they are organized and then organizes so that every quantile for every array becomes the same. So instead of just dividing by the standard deviation, it uses a rank-based method to get rid of variants between different arrays. So just as an example, here we see some microarray data that we got in our group. So I'm going to explain to you what HT and GF means. So here we see HT 2001, 2010. So HT means hypothalamus. So that's just a part of the brain.  We measured the expression of all of the genes. So what we see here is we see here that the data varies a little bit. So the mean of every microarray is slightly different and that is something that we want to get rid of. What we also see is that some microarrays have a very different range because this one ranges from around two all the way up to 16. But you can see that there are some outliers in this array. So when we do quantile normalization, we go from a picture which looks like this. So we have the hypothalamus samples, the gonadophat samples. So what do we do? Well, we normalize it so that every array gets the same mean value and every microarray now has the same more or less variants or the same structure of the data, right? So this is just to get rid of some unwanted variants. However, if you look very closely, you can see that there's something going on here because you can see that the hypothalamus samples, so the HT samples, and we have also extracted GF, which is gonadophat. So when we look at the gonadophat samples, we see that when we compare these four gonadophat samples towards these four hypothalamus samples, we can see that they are all lower compared to the hypothalamus.  Fat tissue is relative simple tissue. There's just one thing while brains are relatively complex tissue, which does a lot of different things. So by normalizing, we are actually normalizing away some of the real biological variants, right? Because if we are interested in genes which are different to the express between gonadophat and hypothalamus, we are removing some because what we are doing is some of the genes which are actually expressed in hypothalamus get pulled down, so they will not be expressed anymore. Well, some genes in gonadophat, which were not really expressed, now get pushed up a little bit. So it seems like they are expressed. So normalization between samples, it is good because we get rid of unwanted variants which we don't want, but we run a great risk of also removing variants in which we are really interested in.  You can look at your raw data more or less and you can look at your normalized data. And based on your raw data, you see that this might not be the best way of normalizing your data. Hey, you might want to keep these two things separate and normalize gonadophat tissue differently than hypothalamus tissue. So to still have the difference in mean after normalization. Hey, but this is something that like no one can tell you how to really handle this. Hey, but the general idea is that you want to get rid of as many technical variation as possible, but you want to keep real biological differences in your data. All right, so the thing that we saw here is real microarray data. And of course, microarray data doesn't come in a scale from zero to 15, right? We're using lasers to scan. So that means that the range is dependent on the amount of lumen or the amount of lux that we get back. And this is very different for different probes, right? Because the different dyes have a different dynamic range.  So the intensity of the Psi5, so of the red channel is not as big as the one from Psi3. The green channel has a much larger dynamic range because the minimum values are in the order of like 2000, while the maximum values, the maximum intensity values are in the order of 40,000. Green is just a much more intense color than red is. So the way to deal with this is just to say, well, we're going to divide it, right? So we're going to take the intensity of the red channel and then divide it by the intensity of the green channel.  So ratios are nothing else than dividing one number by another number and in microarrays, it's very common to take the red channel and divide it by the green channel. So the ratio in this case is the mean of tissue one divided by the mean of tissue two or the mean of the first, so the colors, of course, are the mean of the red channel, which is the disease tissue divided by the mean of the green channel, which is the non-disease tissue. Of course, we now run into a different issue because when we look at ratios, if I look here where both have more or less the same intensity, and then here we can see, oh, this is one, right? So red and green are equally expressed, but then when we look at the slightly greenish dot, we see that we get a ratio of one divided by two, so half. But if we look at the slightly yellowish, so the same intensity change at the other side, now we get a value of two divided by one. And now, of course, we break linearity because going from half to one is plus 0.5, but going from one to two is actually plus one. So we now all of a sudden have a range which is much bigger in the red side of the data than it is in the green side of the data. Going from one to one eighth is minus 7 eighths, but going from one to eight is plus seven. So that's like a massive difference in range. So head to kind of circumvent that. We say, well, we don't directly look at the ratios, but we look at the log two ratios, right? Because what happens now, if we take the log two of the ratios, now we have linearity again. And that means that taking a step from zero to one is the same as taking a step from zero to minus one. So one divided by two is minus one. Two divided by one is one. And so that now means that every step is linear again. So going from equal to completely green is now minus three values. Going from equal to completely red is now positive three values. And that is the reason why when we talk about microarrays, we always have to talk about ratios. Ratios is, of course, to get rid of the difference in ability of a certain dye to generate color, right? Because the green color is just always more intense than the red color. So that's why we do a ratio. And then to make it linear again, so to have a linear relationship, we take a log two ratio. I hope that's clear. I always found this a little bit difficult to explain and to understand. But in the end, it's nothing more than just taking your data and doing two transformations on it. So to have data, which now is unaffected by how well your dye functions. And also, just using the ratio is not good enough because we also want our data to be linear. So if we say plus one, then the minus one, so that the step from here to there should be equally big as the step from here to here, right? OK, I hope that's clear. All right, so now we have microarray data. We know that we have the different intensity colors. We can normalize it. We can take the values, the intensity values, transform them to log twos. And now, what do we want to do? Well, after we've done all of this, we want to use statistics, right? Because we want to show that there's a real difference in the data. And that the data is not just by chance. Tester-Sardis has a question. Do you also have that shirt? No, no, no, no, no. I think it's the wrong way around. If I would have a shirt like this, I would probably want to have it say my F statistics is bigger than your F statistic. The smaller than is a little bit weird, I think. But has statistics are there to determine which things are significant? Oh, god. Always be smart, right? Don't do P values. Do F statistics. They go in the opposite direction. But what is significant? Well, significant just means nothing more than that it is unlikely to have occurred by chance. And that's it.  It represents how likely it is that this is due to chance or not. And for the rest, the P value doesn't really mean anything. So a lot of people think that a P equals 1 times 10 to the minus 16 is a better P value than 1 times 10 to the minus 14. But in this range that we're talking about, like 10 to the minus 14 versus 10 to the minus 16, we're talking about chances which are so small that they're actually more or less the same thing, right? Because the chances of it occurring by chance are just so much smaller than that you want. And so a result is called statistically significant if it has been predicted as unlikely to have occurred by chance alone. So how does this work? So here you see the probability density function. And here we have the set of all possible results. So what does a P value represent? Well, we have the most likely observations in the middle. So we now have observed the data point which is on this side of the distribution. And that means that it is near the very unlikely observations. And then the distance of it towards the really unlikely observation, this is what is called the P value. So it's kind of the area under the curve of this kind of probability density function. So you have to kind of know which probability density function. And then you can use your data point to transform your observed measurement based on the kind of observations which you could have observed. And then had the extremity of this point towards the rest of the data that you have gives you your P value. So probability is a measurement of deviation from random. So that's what it's called a probability value. And in biology, we generally say that we reject the null hypothesis. So the null hypothesis, it occurs just because of chance. Because if I measure something, then it's not always exactly the same thing. So the null hypothesis is rejected if the probability is below a predefined significance level. And we call this the alpha level. So in biology, we agree that the alpha level of 0.05 is good enough. The smaller the P value, the larger the significance. But it doesn't change anything from the results. Because being significant with a P value of 1 times 10 minus 3 versus being significant 1 times 10 minus 4 doesn't mean anything. It just means that both times you have observed something which is unlikely to have occurred by chance. But the smaller the P value, the larger the significance, I think that everyone knows this. Just a little word about the alpha level, right? The alpha level here is a little bit weird in biology. Because if you come from a physics side, right, and you would want to show that gravity exists, right? So you would do an experiment, and you would say, well, I have something which weighs something, and now I drop it. And then it falls down. And then I write down, OK, so I dropped it, and it fell down. And I'm going to then repeat my experiment, right? Because I need to collect observations. So if I drop this thing 20 times and 19 out of 20 times, it falls down to the ground. And one time it just flies off into space, then a biologist would still conclude gravity exists. And that is, of course, very weird. Because if one out of 20 observations that you have would mean that something flies just up into space, that that's not, like for physics, this alpha value is not good enough, right? So for physics, when we want to conclude that gravity is real, we want to have not one out of 19 observations flying into space, we want to have almost zero observations flying into space. Even if we would drop something 1,000 times, the thing flying into space is not going to be good enough, right? Even if we observe once that it flies into space, we would have already concluded, like, that's very strange. That shouldn't happen, right? So the alpha value in biology is relatively high. Many other fields have much more restrictive p-values, like p-values in the order of 1 times 10 minus 6, which is a very basic p-value in physics. And every field has their own kind of when is it significantly different from random? And biology, in that sense, is on the lower side. So we, as a biologist, quite quickly say that this is unlikely to have occurred by chance. For physicists, they are much, much stricter. So statistics are used to determine if significant effects, right? So we are talking about the likelihood that the differences observed are real and unlikely to have occurred by chance alone. So a likelihood is kind of related to probability, but it is not the same. Probability is the measurement of deviation from random. And likelihood is that the differences that you observed are real and unlikely to have occurred by chance. So there's a difference between probability and likelihood. And probability is coming always or likelihood always comes with a confidence interval. Probability itself doesn't have a confidence interval. So when someone says that they have a p-value plus minus something, Matteo Adon, thank you for following. Can people hear the sound for following or not? I don't think there was a sound for you guys. What's going on here? No. All right, so what's going on with my desktop audio then? Oh, it's on my speakers, and I want to have it on my headset. All right, so the next person following should generate a sound. So if anyone could just quickly unfollow and refollow, then they should be able to test it. Now don't all of you guys go following and unfollowing and just making a sound. So there's a difference between probability and likelihood. Probability is, is it different from random? And likelihood is, are the differences that we observed big enough so to say that there is a real difference? And likelihood always comes with a confidence interval, while probability is just a single number. It's just a fixed number. So there is a difference. And remember that there is a difference. All right, so the first statistical test that I want to talk to you about is the student's t-test. It's the most commonly used statistical test in the world, and it compares the difference between two groups. So it is a likelihood test, not a probability test. So the thing that is here, the main thing of the student's t-test is that it requires data, input data, to be normally distributed. In R, it is just the t.test function. So the t.test function in R tells you, if Skorita, thank you for following. This time you hear it, right? I think so. I hope people hear it. I'm not seeing any feedback. Give me some feedback, people. I'm deaf now. Oh, is it that loud? All right. I'm sorry. I will put it slightly down. I heard. OK. No, just kidding. OK, good. Do I still have the? I could have just tested it like this. So. All right, so t-test come in two different ways, or actually in multiple different ways. But the t-test is there's two types of t-test. So you can do a single-sided t-test where your hypothesis is that one group is smaller than a certain group, or that one group is larger than a certain group. But if you don't have any a priori assumptions on what is happening with the groups or with the effect, then you have to do a two-sided t-test. So as an example here, we are studying the Berlin Fettmaus. And it's called Berlin Fettmaus, because the mice generally tend to be bigger than the reference mouse strain. So when we test, if our mice are really bigger than the reference mouse strain, the first time that we tested this, we had to use a two-sided test. Why? Because we observe that they are slightly fatter than the other ones, but we have no proof yet. We have no prior assumption that they are fatter than the normal mouse. So the first time that we did these measurements, we used a two-sided t-test, because we just wanted to show that the group of fat mice was different from the group that was not fat. So after this test succeeded and we proved that indeed these two groups of mice are not equal, then we use a single-sided t-test to prove that the Berlin Fettmaus are really heavier than the standard reference mouse strain. So this is a two-step procedure. And you can only use a single-sided test when you have a priori data. So you have already previous results which show that one group is bigger than the other one or that one group is smaller than the other one. If you don't have prior information, then you always have to do a two-sided t-test. All right, I'm going to five minutes. Been recording for 43 live for OK. I'm going to do another slide and then we're going to take a quick break. So the most common test that you would want to do is the one-sample t-test. So it tests the hypothesis that the measurements that you did is equal to a specific value. So imagine that I have measured 5,000 humans. And now my assumption is that the 5,000 humans that I've measured are, on average, 1 meter, 65 centimeters. That's the thing that I want to know. Then we do a one-sample t-test. So I don't have two groups. I just have one group of measurements. And I have a specified value which I think is the real mean. So what can I then do? And so then the t-test is more or less this. So I have the overall mean of my sample minus the mean that I expected to be divided by S, which is the standard deviation of the sample, divided by the square root of n, which is the square root of the number of samples that I have. And then I get a t-value. And again, I can make this probability density function of the t-distribution. So when you were growing up in high school like me, then you were not allowed to use a graphical calculator, but you had these little books. And in these books, you could look up the critical value for the t-distribution. I don't think that they use that anymore. I think everyone nowadays is allowed to use. But at these books, they just had this probability density function. And what it would tell you is you could look up, and it would tell you that, well, the lowest 5% is at minus 1. The lowest 10% is at minus 0.5. The middle is at 0. And the top 10% is at 0.5 and so on. And then you would do this test.  Then you would look this t-value up in your table. And then in the table, you would see, OK, so my t-value was 0.6. That means that my p-value, so the distribution, the point in the distribution where this value occurs, is at a p-value of 0.1 something or a significant p-value. But this is the most easy test that you can do. You don't have to measure two groups of individuals or two groups of experimental data. You just measure one group of experimental data. And then you test the hypothesis that the population mean is equal to a specific value. So for example, human height, you assume that all humans on average are like 1 meter 65. And then you can just use this t-statistic to calculate if this is really true. All right. Then just look at the slide quickly. I'm going to stop the recording for like 10 minutes. So I will be back at like 3.10. And I hope you guys enjoy the next round of slides. So I will.


Great Ife, great Ife, my brother, my older brother, who attended this university, and I have always had very many arguments about which was the better university, my own alma mater, the University of Lagos, or Ife, or Baphemia Ola University today. This morning he called me and said to me that, well, you are going to Great Ife today. You better hail us, Great Ife. So I have hailed Great Ife, and the thing is that I do so proudly because this is indeed a great university, a great citadel of learning. I bring you the very warm felicitations of Mr. President to the graduates as well as to the university community on your 60th anniversary. As you know, I am at his direction, standing in today as visitor to the university. I also thank you for the kind invitation, a special guest of honor today. The caliber of the honorary graduates at today's ceremony speaks to the high standards of the Baphemia Ola University, speaks to the very high standards of the Baphemia Ola University. And may I congratulate Kaby Esi, the Oni of Ife, Ennito Boussi, or Gia Gia II, a man who has in a few short years redefined the exalted throne of the Oni by deliberately empowering the next generation and building bridges from the west to the east, from the north to the south, and serving our race with passion and zest. Also to Chief Michael Adiojo, I say congratulations, sir, for proving time and time again that you can do business honestly and fairly and still be wealthy and successful, and also by establishing a university and several other altruistic deeds, you have clearly demonstrated that the real value of wealth or status is in the service that is rendered by such means to others. Hurti, congratulations also to those being awarded the Doctor of Philosophy degrees today. Congratulations indeed. I must also pause to celebrate my colleagues in academia, the great scholars and fine academics who make up the faculties here at the OEU. You are the thought leaders at a historic moment where you have the great task of guiding the present and inspiring the future. The convocation lecture of my brother and friend, Arakmuri Oluwarutini, Akreidolu, Senior Advocate of Nigeria, and Supreme Commander of Amoteku, I always describe him a wealthy and successful lawyer and alumnus of this great university. He has given us much to ponder and reflect upon, and I salute you as always for such a thoughtful and insightful lecture, and to the real reason why we are here, the graduates, congratulations to you, and of course all the family, members and friends who are here to witness this great day. A diamond jubilee is certainly worth celebrating, and anyone who owns a diamond would take any opportunity to show off its strength and quality for this university, the Aba Femiah Oluwarutini University, and you'll permit me to refer to it occasionally as if it. There is much to show and many stories to tell. Stories of the institution itself and many of the incredible successes of its alumni. Stories of the triumphs of human endeavor, the primacy of ideas, the creative force of the introspective mind, and the power of vision. For example, that Ife has one of the most beautiful campuses in Africa was a product of vision, and the imagination of the legendary Professor Hezekiah Adidam Oluwarutini, his colleagues and collaborators of that era. And he's in an autobiography, Audacity on the Bound, written by Ambassador Oluwarutini, one of Nigeria's pioneer diplomats who studied at Harvard University around the same time as Professor Oluwarutini, and who later became a professorial fellow here at the OEU. He says, and I quote, the magnificent Aba Femiah Oluwarutini University was the toil and sweat of Hezekiah Oluwarutini, and I think it is fitting, and I think it is fitting indeed that the university library, the symbol of learning, is named after this great man. This university OEU is, and continues to be very much, a bastion of progressivism and innovation. And not surprisingly, you will find the phrase, a looter against all oppression, in the great Ife anthem. This progressivism is evident in the outlook of staff and students alike, including alumni who are governors, like my learned daughter. The great Ife Socialist School of Thought, the Ife Collector, with seminal thinkers like Shekmao Shobar, Shesandikwe Oluw, Toyo Oluwarutini, Ola Dupo Fasino, Shekmao Oluwai, Diji Daru, and others, were here, mutually reinforcing their counterparts at the Amadu Bello University. It was here at Ife that our Chief Aba Femiah Oluw for many years Chancellor of this university gave some of the most memorable and consequential lectures on the political economy of Nigeria and addressed some of his most crucial problems, including the imperatives of democracy, national economic development, the ideology of governance, and of course the famous National Sensors Figures. It was while still teaching here that Professor Oluwai Oluwai Nkau won the Nobel Prize for Literature in 1986, but perhaps is less well known that he was also a lecturer at the University of Ife Ibadu Campus in the early 60s when he started his earliest commissions with state authorities. And it seems that Oluwai Nkau attracted a new tribe of literary insurgents into Ife. Insurgents because they represented a more aggressive politically and socially conscious literate than their forebears at Ibadu at the time. I'm talking of the likes of Biodu J4, Folio Motorshow, Yemio Gubii, and again Diji Daru. Yemio Gubii and Biodu J4 and a few more left if they had joined Alexigru and Stanley Makibu and Lade Bonoala to radically and permanently change the face of print journalism in Nigeria with the establishment of the Guardian newspapers. But that was a generation of men who are now 70 years old and over. Now several generations later, sitting in his hotel, his postal room here at the OE, Sheung Oshewa, then an undergraduate developed a revolutionary journalism idea, this time technology-based, Naira Land, now possibly Africa's largest internet forum. Naira Land has 2.5 million registered users, almost 10 times more readers than all Nigerian print newspapers put together. But talking about founding businesses in hostels, I'm sure we all know now the multi-million dollar job website, Joberman. It was founded also here at the OEU in 2009 by Ola Lekmoy Lude Ayode Jadyo Umi and Okwemi Aweyemi. At the time, students of this university, it was here at IFE also. In this place of deep culture, that the Orioloku Acting Company was founded by Professor Ola Rotimi from where the award-winning dramatic tragedies, Hurumi, and the thoughts are not to blame, came. And from here also, held more recently, and from here also came more recently, the eloquent historical excursions of Professor Tui Falola, who is currently at the University of Texas at Austin in the U.S. These pioneers and greats inspired a generation of artists that established Nigeria's primacy in the creative arts. Young artists trained at OEU are continuing in this strong literary tradition. Lagbaja, the iconic masked musician, is also an IFE alumnus whose father was faculty here. Damia Jair, who studied medicine here, but went on to co-found Saraba Magazine. Emmanuel Faith, an up-and-coming poet, who is helping to promote reading culture through bookathon, where members read at least five books per month. But the stories of IFE go beyond its history. IFE has always proved to be a place of cutting-edge innovation and even more so a place for the incubation of tomorrow's solutions. In 1974, at a convocation ceremony held here at IFE, Tui Falola captured the centrality of man in the advancement of our world. When IFE was to be said, and I quote, man is the sole dynamic in nature and accordingly every individual in Nigeria constitutes the supreme economic potential which this country possesses. So we must be proud of the far-sightedness of the faculties and management of this great citadel of learning. OEU was very much ahead of its time when it named its medical faculty the Faculty of Health Sciences and its engineering faculty the Faculty of Technology. The Faculty of Health Sciences at IFE was unique from the emphasis that it laid on community medicine and the attainment of a BSc in health sciences before proceeding to study for the medical degree of MBCHP. So not surprisingly, the university has produced top-notch researchers in several areas of medicine, but in particular in public health. I had the pleasure of meeting with Professors Agisiria Guy and Thelma Bavarola, both IFE alumni, when they undertook groundbreaking research and clinical trials into the potential use of Ivor McTeen as a prophylaxis and a cure for COVID-19. Professor Louis Inca Olutoev, another alumnus of the Faculty of Health Sciences, attained global recognition when he led a team of surgeons to successfully take a 23-week old baby out of her mother's womb, removed the tumour from the baby and returned it to the mother's womb where the injuries from her operation healed and she continued to grow until she was born the second time at 36 weeks. The Faculty of Technology was one of the first to include electronics in its electrical engineering programme and so not surprisingly, the early leaders in technology and tech-enabled businesses were alumni of OE's Department of Electronics and Electrical Engineering and of course you would mention the lives of Shegun Ogusoy, CEO of Airtel Africa, of course is an IFE alumnus, Halturi Ola, CEO of MTA Nigeria and another great IFE alumnus and product of this same department, Professor Kintayo Akiwande, who teaches today at the Massachusetts Institute of Technology and is one of the world's most respected professors of electrical engineering and computer science. And only last week, another alumnus of the Electronics and Electrical Engineering Department of this university, Ms. Funker O'Quaker, made global investment news when Equinix, the global conglomerate announced that it was acquiring Main One, the company that Funker founded, for $320 million. Main One is West Africa's first privately owned open access undersea high capacity cable. It is 7,000 kilometre cable stretching from Portugal to West Africa with landings in Accra, in Dhaka, in Abidjan, in Lagos. And I'm sure that the Department of Agricultural Economics must be wondering how I forgot to mention the two most celebrated alumnus, Dr. Akiwande Deshina, president of the Africa Development Bank and winner of the World Food Prize and Dr. O.K. Orama, president of Africa Exim Bank, who, of course as you know, obtained his MSc and PhD here at OAU. So to the graduates today, you have some of the highest shoulders and the broadest shoulders to stand on. But it is this generation that must deal with the biggest issues that confront the world and confront our nation. It is the big and innovative ideas that will solve these problems. You will have to confront the problems of climate change and a world moving away from fossil fuels. And you will usher in the age of renewable energy and green solutions. You will deal with the issues of feeding, educating, providing health care and jobs for the fourth largest population in the world in a few years to come. We will need smarter, aggregate solutions to feed these huge numbers. Technology is already helping to crowd fund agriculture and help more prolific seedlings. Education needs hundreds of new solutions. We will not learn and teach in the old ways in another few years. And we have to design ways of teaching millions, even outside the classrooms. And there are many young men and women already doing great things, using technology to reach children in far-flung areas of our country. You will confront the need to vastly improve our public and clinical health care. We must build on the work of the Genomic Centre at EDE and the local vaccine production efforts going on and make local drugs, make drugs locally for hundreds of millions of Nigerians. The insecurity problems that we are experiencing, the rise of terrorism in several parts of the country, this very large country and the access to lethal weaponry by non-state actors tell us that we must be smarter in policing the country, using smart drones and surveillance equipment. The politicization, if I may, of the importation of ammunition tells us that we must manufacture our own arms. Already, Pro Force, a Nigerian company led by Ola Woundey, is manufacturing APCs and more personnel carriers and MRAPs in their factory in Uderemo, and they are exporting already to several African countries. So are Imperial, a company based in Paduna, and the government own Daikon, producing different types of ammunition. The future is smart weapons benefiting from AI and machine learning. Yes, the challenges are huge, but I believe that you are well equipped to resolve them. And the evidence is here already. Since 2016, despite two recessions, young Nigerians have built six unicorns. A unicorn is a company that is valued at over a billion dollars, and there are already six established in this country since 2016. It was our who said, right here in IFAE again, that it is effective economic planning and even more effective implementation that would enable us to avoid a disaster and reap phenomenal progress instead. And so our third national development plan 2021 to 2025 is an attempt to chart a path for the future. The future belongs far more to you, graduands, so you need to pay attention to it. One of the crafters of the plan, Ambassador Yermy Diquielu is here with me, a special advisor to the president of the economy. He is also an alumnus of IFAE who only lived in this campus all his life. His father, Mr. Asha Sondiquielu of Blessed Memory, was IFAE's first African Liberia. The strategic objectives of the national development plan include establishing a strong foundation for a diversified economy, investing in critical infrastructure, in particular power and broadband, enabling human capital development, teaching STEM, and improving governance and strengthening security. And its implementation is expected to be supported by a range of measures of fiscal, monetary and trade measures. The plan emphasizes the creation of an average of five million jobs per annum during the period. In addition to job openings, it's also essential for Nigerian youth to acquire the skills and knowledge of the workplace. This is why we're working with the United Nations Development Program and the European Union and other partners on the Jubilees Fellows Program, which is a one-year work placement scheme for 20,000 young Nigerians annually over the next five years. This will be a paid work placement scheme to sharpen the skills of those who are coming out of school beginning from the first quarter of next year. The national development plan focuses on value addition across all sectors, including agriculture, manufacturing, solid minerals, digital services, tourism, hospitality, sports and entertainment. In agriculture, for example, equal attention is being given to primary production, as well as other aspects of the value chain, such as transportation, storage, processing, marketing and exports. In a similar context, the plan places great emphasis on the export of goods and services, especially leveraging on the African continental free trade agreement. There are several, several different aspects of the plan, and I hope you'll get an opportunity to relive on the summary of it. So let me again congratulate the honorees and the graduands, and I pray that you individually will do much better than all your producer cells that we have mentioned here today. And to the University at 60. And to the University at 60. I pray that the next 60 years will be more fruitful and more fulfilling and make this an even greater university. God bless your family and all of the University. God bless the Federal Republic of Nigeria.


What's up guys? I'm Brian Sakawa. You're watching He Spoke Style and today I'm sharing five of my favorite fall fragrances. Before we get started I want to let you know that this video is presented in partnership with Scentbox. So this is my absolute favorite time of year. Love the crisp air in the morning. Also my favorite season for menswear. And as the seasons change we need to think about changing up the fragrance that we wear. We want to be moving from lighter and crisper scents into something with a little bit more body and maybe some spice as well. So the fragrances I chose I would say are really great early fall fragrances. Great for that transition time so let's check them out. My first pick is by Aqua Deparma and this is Colonia Ebano. So interestingly I can't remember when I got this bottle maybe two or three years ago but as I was preparing for this video I went to look up this fragrance on the Aqua Deparma website and I couldn't find it. I don't know if they discontinued it or not but I see that it is still available from a few places online. If it has been discontinued I think that's a shame because this is a really really nice fall fragrance. So forget everything you know about Aqua Deparma and the original Colonia. This is nothing like that. Original Colonia, fresh and citrusy, Ebano is woody, it's spicy and it's aromatic. Top notes are listed as bergamot and pettigrein but I honestly don't smell those at all. Now what does come through a lot on the very first spray is pepper and that peppery spiciness stays prominent for about the first 30 minutes or so on my skin. As it dries down it takes on a much smoother woody scent. Almost kind of a sweet wood but not overly sweet. It's not cloying in any way so personally I wish the pepper would stick around a little bit longer but overall I really like this fragrance. I would say it has about a medium projection and a medium to long lasting staying power. Next up is B683 by Marc-Antoine Bois. Now this fragrance is leathery and woody but a very very elegant and refined version of those two scents. It was actually inspired by his and the perfumer's childhood memories of smelling leather like from an attaché case or a desk blotter. You know just imagine the kind of wood paneled room that this would be in and you'll instantly have a picture of what this smells like. Now this is a leathery fragrance but again it's not an intense leather it's very I wouldn't say subtle it's definitely not in your face it's just like this nice blanket that kind of envelops you. On the skin it doesn't change much from the first spray to when it dries down which is not a bad thing in my opinion there are some people who don't like that. It smells so nice that I wouldn't want it to change so this one's new for me I really like it I've been wearing it a lot I would say this has become my go-to fall fragrance possibly on its way to becoming my quote-unquote signature scent. Now from leathery to a more kind of classic spiciness we have Musco Real Spiced Citrus. This is classified as an exotic Oriento Fugère. You have a nice combination of freshness with the citrus notes and a warmth as a result of the spices and incense. This is not a super complex fragrance top notes are lime, coriander, lemon, and verbena. Heart notes they say are simply spices and base notes are wood and incense. If you're a fan of barbershop fragrances this is one for you and a great fall version of a barbershop fragrance. My only gripe with this is that it doesn't last very long you might get two hours out of it but what a great two hours those will be. This would be nice to splash on after work if you're meeting someone for drinks or a like kind of casual dinner. Next is one that I've included in roundups before. It's an all-time favorite of mine. This is Federique Molle's Begarade Concentrate. This is supposedly an ode to an ode to cologne but one with decidedly more depth and spice. So right off the bat you're hit with a wonderful bitter orange. It almost smells to me like if you've ever had a flamed orange peel as a cocktail garnish take a second and just imagine that and then it moves more into the spiciness which you get from a hint of cardamom and pink pepper and the base of this like I said this fragrance has more depth is more woody and you do get notes of cedar and a little bit of hay. This is a very long lasting fragrance easily will last you all day long and although it will last all day it's not an overpowering fragrance. I really like what they say about this one on the website the last sentence in particular and that's that Begarade Concentrate is for those who believe that seduction is found in cleanliness and perfection. My last pick is a fragrance by Tom Ford this is Italian Cyprus. Now I have to say that I was extremely close to putting Ralph Lauren Polo Green in this roundup it's a classic it's a great fragrance it's great for fall it's great for winter but I thought that was a little obvious. So I chose something a little less obvious but definitely something that I would put as very comparable to Polo Green and that of course is Italian Cyprus. I did a quick little comparison between the two and what I found is that Italian Cyprus is a little brighter it's got more zest it's got more punch where I would say Polo Green is a little more smooth around the edges this one is refined but I would say also a little wilder and I think that really sort of encapsulates the difference between the two brands when you think about the brand identity a great everyday fragrance and perfect for fall. Now remember that the way a fragrance smells is very dependent on you your skin your chemistry I would always advise you before committing to buy a full bottle and investing in that that you test out the fragrance on your skin going to the mall going to a boutique and spraying on your skin walking around and then making a determination that's one way to do it however I found it's best to really sort of live with that fragrance and that means having more than just one spray on one day and that's where a sponsor for this video Scentbox comes in. Scentbox is a monthly subscription fragrance company basically an opportunity for you to try out new fragrances every month they've got lots of fragrances on their website you choose to get a 30 day supply of each in a travel size atomizer best part about this is that if you don't like something you don't have to wait an entire month to get something new you can just go ahead and exchange it for another one quickly and easily and completely free of charge they've got great customer service and it's a great way to try out new fragrances so those are my five picks for great fall and fall transition time fragrances I'll list them all down below in the description go ahead and leave me your favorites as you know I love to find out about new fragrances that I haven't heard of thumbs up if you like this video hit that subscribe button and until next time thanks for watching and stay tailored


Now, we will see how this equality is focused in early childhood. First, we see the difference between boys and girls and it is that difference between physical and mental growth rate, nature differences, differences in study habits and other physical working, but both have the ability to bear difficulties and face them bravely. As we have been discussing previously, boys and girls are physically and psychologically different from each other, and their growth rate can also be different, their interest rates can also be different, their attention spans can also be different, and their interest as I said can also be different, their game can also be different, but this does not mean that none of them is capable of doing anything, they are capable of doing everything, if they are given a chance and they are helped, so in such a situation we see that both can solve the problems, can solve the problem, and both can keep courage. Then is reflection or gender equality. Look, the first thing is that we have to reflect on how we are dealing with our kids and children, how we have to deal with our children. Look, wherever the protection our children need, we have to give it to them. What kind of protection do children need? What kind of protection do children need? We have to teach them what kind of skills they need, what kind of leadership qualities they need to have, how to mitigate their differences. This is a reflective process. We will think about these things in the form of a child. Now look, the reflection is basically three questions. The first question is what are we doing or what has happened? The second question is what is the reason for what we are doing? What was the purpose? Why did we do it? And the last question is what is happening? What happened? What did we learn? What did we get? That is, you will always mention the reflection. So these three things will come to light. Now we are telling you why this movement came to light. So feminist movement in the 1960s and 70s to gain equality for women. As I said, in the 20th century, when one thing goes against one another, then the movement comes against it. There was a time when there was no country in the world where there was no gender discrimination. There were so many injustices. So now those who we call developed countries were also victims of this. People were being made slaves unnecessarily. Especially women's rights were violated. Children's rights were violated. So such a movement came that all the oppressors and oppressed groups, especially you must have seen in the world war how people's rights were violated.  A fair and even-handed approach to show equal levels of care, respect and other concerns. And people said, that women should have a connection with any area. They should have a connection with any gender. They should be small, they should be big, they should be men. Everyone should have equal rights.  Everyone should have safety. And basic necessities, food, sleep, and home are all needed. And everyone should get it. You know, the main purpose of wars is to violate the rights of others. So gender equality in practice, this was discussed in the 20th century on gender equality. The basic aim is that both males and females feel positive about their identities and both have equal rights. Okay? We are not talking about reducing anyone here. But we are saying that every person should have a dream, be it a woman, be it a man, be it a child, be it a girl. Everyone should have the right to meet their needs and to get equal respect and respect. And not to make anyone a slave for no reason. Okay? This happened a lot, that when there are wars in many countries, then you know that a lot of rights are violated, people get caught, and they are faced with various problems. So at that time, in the world, the United Nations, and all these people, their leaders, were united. And they campaigned that all the people in the world have equal rights. They are from some country, from some nation, from some gender. All of them should have equal rights. So now the things that are coming, now this whole campaign, in 21st century, especially with early childhood, has been done, has been associated, that since we understand that these attitudes of adults cannot be changed, cannot be improved. In fact, by educating children, one can get a healthy mind and equal justice society. That is why, this has been encouraged a lot on children's education.


Hi y'all, she was seven. Okay, so someone wrote to me on my page It was a guy on my Facebook and he was telling me the story about this girl that he you know met and asked her out to coffee to Starbucks as like I guess their first date, but he called it a meet-and-greet and He says that the girl met him at Starbucks ordered her coffee and he only had $23 and And So he ordered her coffee paid for her coffee and she told him that she's used to going out to dinner on her first date she was hungry and You know, he told her to buy herself a sandwich and then she proceeded to say well I'm used to guys taking me out whining and dining me my ex-boyfriend.   Y'all can laugh Let me just reach all the thing so y'all can get the whole story. I gotta put on my glasses. Okay, so here's the story I have $23.99 in my pockets went to meet up with a younger chick at Starbucks Paid for our iced coffee during our meet-and-greet. She kept talking about how She's about the club life About having fun. She normally don't do dates at Starbucks Could we go out to eat? I'm like shorty. This isn't a date. This is a meet-and-greet. If you're hungry. They sell sandwiches. You can buy your own Food I'm not hungry. I told her after work. I mostly go home and take care of personal business I don't have time for clubs. I have a bar in my own house Shorty says I sound born as if Found out she didn't have a job She let her ex-boyfriend Take care of her Now her parents says I sound broke What woman wants to do that can't pay for dinner, I'm like be you're 22 years old You have no job. I have my own place And you aren't nowhere near the same level as I But hey, take care of your little a No, take your little a home and eat your parents food That's what he told her Okay, so y'all want to hear my response Okay, my response was lol Well, the sad and funny thing is that next month She will probably meet a man who will wine and dine her and take care of Her because she expects that and will eventually attract the type of man who wants to do those things for a woman Believe me lol. I met the man who I married when I was 22 He wine and dined me paid all my bills Brought me bought me a car and took me on trips. We are still married to this day Most women know what they want in a man and they choose And they choose not to stray or let down their standards because they just might meet the man that they that will cater to them I would never suggest that a woman lower her personal standards men. However have more time Than women to get their money up and have a family Women are on a schedule and a time clock to have kids and a family So we have to be sure and direct in the direction We are going in life wasting our time with a man who does not meet our standards It's pointless Not to say that you are not a great man Just saying that when a woman seeks a man to take care of her she cannot waste her time on one who is not willing No harm done. She was just not the one for you and you were not the one for her Okay, and then he writes back lol. Yeah, that's how a lot of men think today I feel sorry for these young girls Don't feel sorry for them. They end up like me So I don't want to let y'all know how these guys are thinking They're trying they're trying to take you out on a date with $23 in their pockets are approached by somebody And they ask you out on a date knowing that they only have $23 in their pocket What kind of planning is that you know, what kind of husband he's gonna make Not a very good one. You don't ask someone on a date when you only have $23.  Oh, he's gonna take me out and then you take her to Starbucks with $23. No, okay I know a lot of women who say well, I will do coffee dates. Well, that's y'all I don't do coffee dates unless I'm meeting my friend. Who's a girl or Gay guy, whatever.  Basically, it's what we're saying. I don't know if I'm ready to spend money on you yet Meaning I don't know if you're worth it yet If you don't know I'm worth it by now, then you'll never know You got to take that chance, you know ladies when you have that self-esteem about yourself You don't do coffee dates darling. You do you see I can meet you at the bar. We could have a drink Because when you get up and leave because you can't afford to buy me drink number two somebody else is okay That's what I'm saying. Don't meet at a coffee bar meet at a real bar If you're gonna just meet out meet at a real bar So so they can see all the man Google and grovel over you.  Okay, I love Starbucks God knows I love Starbucks. I would never have gone on the first date there You're gonna take me to a real restaurant and a real bar so you can see other man wanting me Okay, so you can know my value and my worth. You're not gonna take me to Starbucks.  No, you're taking me somewhere where I want to go. First of all, if you ask me out second of all You're gonna get to watch other people watch me and Empty you so you can feel good about yourself because of me. Okay. Now. I know that sounds conceited as Get out But that's just the truth. Okay, that's why I'm do Starbucks coffee dates never had Never had to I've had someone ask me. Oh, you want to meet for coffee?  How about we beat for a drink a real drink and they'll laugh like Okay, well, maybe That means they got to go to the ATM or something. I don't know but Ladies don't sell yourself short. Don't sell yourself for a cup of coffee. Don't do it. It's not worth it and Somebody asked me this before if you do accept a date to Starbucks Make sure you bring your friend So they know it's not a date either. Okay It's a meet and greet and I want you to meet my friend, too Okay, it's someone if someone has the audacity to invite you on a first date for coffee bring your friend That way he knows This is not a date if you want a date if you want me alone If you want me to take the effort and time to come out of my busy schedule to meet you Make it worth my while because I'm busy shopping with my girl And we both need a coffee right about now because we are pooped. Thanks for buying our coffee meet and greet Thank you. I'll see you later if you want to go on a real date. Give me a call when you get some money, okay? I don't y'all I'm just I'm just having fun, but like seriously Coffee dates if you if if you're seeking someone who You want to really be in a relationship with who you want to really You know Get to know You have to back down all of their approaches That don't please you Because otherwise you'll just be wasting your time And when a real man steps up to you he's going to have reservations To a place that has ballet If not ballet just to a nice place at least that the person who works there opens the door for you in the restaurant You know So don't sell yourself shortly.  Don't sell yourself short Sorry y'all I got cut off. Okay, so let's just break it down This was an African-American man an African-American woman now Let's just pretend this was a Caucasian lady or a Latina lady Do you think that he would have invited her to Starbucks to meet as well? I'm not trying to be racist or anything because I drink Starbucks. Yeah, I've been drinking Starbucks, but Typically, you know a person who goes to Starbucks every day and gets their coffee because they can afford it It's not gonna be impressed with Meeting at Starbucks for a date Because it's like a daily thing. It's like a daily routine. It's like, okay, I gotta go get my Starbucks Okay, I'm gonna go get my Starbucks. I gotta get my Starbucks day after day after day Now some guy comes up to you and he's trying to impress you and say, oh, do you want to meet at Starbucks? I'm like, no, I've already been there today. Let's try somewhere else. You know if this lady would have You know frequented Starbucks on the daily. She would have automatically said, you know, I've already been there today You know, can we do something else? Can we go to a bar? Can we go for tapas sushi something?  Don't even He might have been honest, but he was honest in in a way that let you know to cut him off Okay, if some if a guy It means to you that he only has $23 to take you on a date Cut him off. Why?  Okay. No You know what I would say I would say well, you know what?  That is so sweet. I'm gonna I'm gonna build him up, right? But you know, I would rather us go on a nice date At a nice restaurant and have some drinks so that we can really get to know each other a lot better So, you know when you get paid My number will still be the same and we can meet out But I really don't want to go out with you and spend your last $23 because what if you need that for an emergency? I'm just I'm gonna be like super embarrassing him while being nice at the same time. It's called nice nasty Nicety so I'm gonna be like well, I don't want you to be without for the week You know, and I don't want to I don't want to dip into your rent money or anything just to take me out You know, I would rather just wait until you can afford it You know put him in his place because that's where he needs to be and that's what he needs to hear He doesn't need to hear. Okay. I'll meet you in Starbucks. Even though y'all only have $23 I will feel guilty meeting somebody at Starbucks and they only had $23 to their name until payday I don't want to take your money. No, I would be like, you know what? That's okay, baby. That's okay I don't want you look twin.  Okay? That's why I could never broke that's why I never could date broke guys Because I felt guilty, you know, they try to spend all their little Change their rent money Trying to impress you which they're not doing and then they expect something in return because they just spent half their rent On you and you're just used to that.  So you know what? Let's just meet at a parking lot because that's the same thing They say, you know what you let's skip Starbucks. Let's just meet in the parking lot And he's gonna be like really you don't want any coffee like no, I'm good. I had some this morning And I just had some while you calling me, you know, I have one right now It's like I have a coffee right now. I just came from Starbucks So it's gonna go. Oh Okay, so y'all are gonna meet in the parking lot and You get to see his car too girl. So you already know You get up to so you get up to the parking lot and meet him see what he's about Okay. Well It was nice to meet you Maybe we can do it again. We have some money Anyway, I'm gonna let y'all go cuz I know I'm gonna get a lot of bad comments in this video And I'm just I'm ready to bring it on


Good afternoon. Good evening, everyone. Thank you for being here. It's a great honor to host President Santos of Colombia in our nation's capital today. We just wrapped up an excellent bilateral meeting, and we look forward to a state dinner hosted by the Governor-General later this evening. Since 1953, Canada and Colombia have enjoyed a strong and vibrant diplomatic partnership rooted in economic cooperation and development efforts. Our countries understand the importance of full and frank dialogue in achieving progress on things like human rights, poverty reduction, and security. We also know that the way forward in the modern global economy is through partnership. On trade, for instance, we have a truly balanced relationship facilitated by the successful Canada-Colombia Free Trade Agreement. This agreement has been enforced since August 2011, and we've seen tremendous benefits for both of our countries as a result. And our efforts are not contained to our own interests. Canada and Colombia work together in advancing shared interests at organizations like the United Nations and the Organization of American States. Today, the President and I agreed on a number of measures that will advance peace and strengthen our bilateral relationship. Let me tell you about just a few of those measures. First, we've agreed to work together to establish a bilateral police initiative. This effort will support post-conflict policing efforts in Colombia and will see Canadian police providing training, capacity-building, and strategic advice to our Colombian friends. Canada is also pleased to announce that we will be deploying one of our government experts in Colombia to engage in important work alongside the Office of the UN High Commissioner for Human Rights. These initiatives build on the substantial assistance Canada has provided in support of Colombia's peace process. In order to continue to encourage economic growth, trade, and innovation in our two countries, we have taken, last week, negotiations in order to conclude a free trade agreement between Canada and the Pacific Alliance. These negotiations are an excellent opportunity for us to establish the standards of a progressive and modern agreement that will benefit all the citizens of our two countries. Moreover, we intend to enlarge our cooperation in the field of air transport through a bilateral agreement. The relationship between Canada and Colombia is strong, bolstered by a dynamic Colombian-Canadian community of over 76,000 people. We understand that, in working together, we can create opportunity and prosperity for our people, as well as a safer, more secure world for us all. In closing, I want to say a few words about President Santos specifically. As a fierce advocate for peace and security, the President has been resolute in calling for justice and stability in Colombia and, indeed, across the region. With a long history of service to his people and to his country, we are honoured to host him here in Canada, and we will continue to be a steadfast partner for a lasting and durable peace in Colombia.


hey gems welcome back to my channel so based on the title you already know what this video is about so first I'm gonna show you guys how we prep the placenta to avoid it from smelling while waiting for the cord to fall off so first we rinse the placenta with water to get as much of the blood and blood clots off as possible then I applied my herb mixture at the bottom and then place the placenta on top of it then I dropped a few drops of essential oils and that included lemongrass, rosemary and lavender. I watched a few videos on how others did this but I decided to use both essential oils and dry herbs. The dry herb mixture you're seeing right now is a mixture of rosemary, thyme, spearmint and basil as well as chamomile. This mixture smelled very fragrant and would have been enough by itself but me being extra at the moment I decided to also add rose petals and lavender flowers so our entire room was just smelling very fragrant very aromatic because these dry herbs were like I said very fragrant. We're also going to be adding sea salt but we're gonna do that on the second day because the sea salt is to help dry up the placenta itself and I wanted the baby to get as much from the placenta as possible before adding anything to help dry it up so that is about it on how we prep the placenta in the next few clips you're gonna see the process of the cord drying up and the placenta falling off. If you're thinking about doing a lotus bird but concerned about it smelling just use a lot of fragrant herbs and you will have no issue. So this is day two of our lotus bird and look how much this middle part has already dried up because it's more exposed to air so right in here just has a little bit more drying to do and then of course the cord closer to his navel which is already drying up. Today we're actually gonna add sea salt to help the drying process I didn't want to add it on the first day because I wanted I wanted him to just continue to get everything possible from the placenta so last night as you see as you saw in the previous clip we only added the herbs and essential oils which just smells so it smells so good and he's been so peaceful he pretty much slept throughout the night look how much the cord has already dried this is literally just he was born literally yesterday and the cord is already pretty much dried I forgot to record changing this but all we did was take out the gauze and change the fabric and we laid this is actually our underpad that we cut and we laid more fresh herbs sea salt then we placed the placenta and then we put more sea salt and fresh herbs on top it's already so dry this is literally just one day progress is being made today is day three of this little spirit and the cord is gonna fall off at any moment probably isn't that today I'm thinking tomorrow so last night all I did was add some more essential oils to the placenta on top of the herbs today I'm gonna do a full change again in terms of changing the herbs and the padding so far so good so day five and the cord is finally off so now we're going to bury the well plant the placenta what are we doing with a tree yeah we're gonna bury it under a tree well plant a tree on top of the placenta that makes more sense right so it's now for this isn't able to heal and everything is all dried up and of course the placenta is buried under all of these herbs so we're just gonna take it out dig a hole put the placenta in and then plant the tree on top of it and I will show you guys when we're doing that which would probably be on Saturday today's Thursday so what we're gonna do with the placenta is bury it by planting a tree on top of it I'm going to buy an avocado tree from the market and we're gonna plant it in the yard in my grandmother yard so yeah he's finally free of it this was an amazing experience I'm not gonna lie however I am happy it has finally fallen off cuz it's so much easier to maneuver him so we just had our little ceremony and now we're about to plant the placenta right here still covered in herbs mind how you take it out I didn't make a river, it's a Babylonian river. It's a Babylonian river right now, it's living in a Babylonian river for too long. The placenta avocado tree, the cliff you just saw was that happening it's late but it's not midnight so you know it's still Saturday the day we did everything today, the main ceremony and planting the placenta so everything is complete so yeah, it's been a great experience I hope you guys enjoyed me sharing clips of my labor and delivery process and even though I wasn't able to show everything that I wanted to I hope you enjoyed it when I was able to share and that was me in my next video so this is baby's navel all healed up no complications, no issues, no infections, a healthy successful lotus berth so if you want to try a lotus berth but haven't second thought so are you scared to do so give it a try it was an amazing experience and it's something that I will do again I hope you guys enjoyed this video let me wrap this up before he starts to cry but don't forget to like comment and subscribe please subscribe and support me and you guys will see me in my next video you deserve it all this is perfect and it's magic and everything you do is enough


Hello everybody, E here. Welcome back to another Stephen King review. Today we are talking about Stephen King's third published novel and that is The Shining in all its many forms. This is the original movie tie-in cover for the Kubrick film. This is just a reissue that's anchor books. And then I'll go ahead and show you guys the original double day first edition with the artwork that wraps all the way around to there. Very nice edition. Very nice, very nice. But the one that we are going to be talking today, the one that we are going to be talking about today is going to be the Cemetery Dance double day edition and that is this one. I already took it all out. I will be doing, not unboxings, but I'll be doing showcases. If you've seen my ones for Horns, Joe Hill's Horns, for Sleeping Beauties, it's right here and Nostrautu. I'm going to be doing another one for the double day series as we go along through that. I have live unboxings of those things but the quality just isn't very good so I'm going to go back to them again. I'm going to do Carrie, Salem's Lot and then The Shining. But the reason why we're talking about this one today in the spoiler review is because I wanted to talk about some things that happened at the beginning and end of the book that is not in any other edition of the book and that is of course before the play and after the play. After the play was never published at all, never included in the book in any form other than the Cemetery Dance edition and that white is blindly white. But before the play was included with TV guide, oddly enough, it was included in there. So what is in before the play? I wish I could get the rights to just read it to you guys but I cannot and also every time I see this it tickles me. But a long time ago in a galaxy far far away, Stephen King's third book, he says this is for Joe Hill King who shines on. Not Joseph Hillstrom King but for Joe Hill King as if he knew his son was going to be using the Joe Hill name. But it's funny that that was there the entire time that Joe Hill was writing and publishing 20th century ghosts, heart shaped box and horns and nobody caught that. Not even myself. It's a funny side note for that one is I bought 20th century no I bought heart shaped box and horns without knowing that Joe Hill was King's son and I ended up loving him but as I was reading horns I was like this feels awfully familiar and I couldn't place who. So the introduction is by Stephen King. I highly suggest if you have this version of the book that you read everything in here except for the cut stuff that the editorial process did because there's even a section in here where a famous Stephen King collector gives the editorial notes from the original edit of the book and that's probably the only boring part here but in the introduction King talks about his inspiration for the book and what he was trying to accomplish this time around. He wanted to reach higher than he had reached before with Carrie and Salem's Lot. In fact I think in another introduction for the shining I believe it's in this edition it might not be it might be in another one. He talks about how if there comes a time in every author's career when he has to decide whether or not he's going to keep on writing the same old thing over and over again or if he's going to try and reach farther and that's what King did and as far as I'm concerned the shining is the first real Stephen King book. Carrie and Salem's Lot they feel like an author trying to find his voice and then the shining you really really feel that voice but before the play is broken up into five different sections different scenes I should say and the first one is the third floor of a resort hotel fallen upon hard times there's Bob T. Watson who is the I believe it's I believe that's his name but anyways it's the guy who I'm going to stumble on names I'm going to give you the overview of it the the in the in the book itself you you meet the guy who works on the boiler he is the one who shows Jack how to fix the how to dump the boiler so that it doesn't explode well his great grandfather a great great I can't remember exactly what it is maybe it was his daddy I can't remember right now off the top of my head but they are the one he was the one who actually built the hotel and when he built the hotel there was financial struggle after financial struggle and every time he seemed to be getting ahead something would happen they had to repay the road three times there's several things just over and over and over again he never got out of the red so he finally sold it to another individual I think his name was Paris sold it to another individual and that individual ended up going into the the red also or was in the red to begin with is just all the troubles that came of it how some of the ghosts got there extended scenes from like Grady was another one that I enjoyed quite a bit there's so much in before the play that I really really loved that I wish was in every additional because I'd like to talk to you know my friends about why this why this intro should never been cut out of the book now as far as whether or not it was cut out or if it was added later on to to fit in give something to add into the movie time I don't know I'm not that much of a historian where Stephen King is concerned that I can answer those questions but and then scene two a bedroom in the wee hours of the morning is a they're basically short stories from the different people who either either built owned or stayed at the overlook hotel and this one's a kitty no not kitty sorry Lottie kill kill gallon k i l gallon g a l l o n uh believe she is either a rich woman or an actress I can't remember which but that one was really good you get to find uh there's there's illusions too later on in the book where Watson says the boiler she creeps I think is the line and how her stay at the hotel haunted her for basically the rest of her life and then there's scene three on the night of the grand masquerade and that has no it wasn't great he was horse derwent uh the I think it was the owner just before the newest owners came in uh where the book is I can't actually remember I know Grady was a caretaker uh well while horse derwent owned the place but how Horace uh swung both ways and all that goodness so and there's much about what happens uh after just before the his boy toy as it were ends up committing suicide or does he dumb dumb dumb by taking a bunch of pills and then scene four and now this word from New Hampshire it actually goes and talk this is a scene that really should have been in the book where you see Jack Torrance as a boy having to deal with his own father and you're seeing in that scene you see a child who loves his father even though his father is a monster I find that that whole that whole scene is great to me and it really should have been in the book because king says himself that that is the theme of the shining it's loving a monster is how you can continue how a child can continue to love a parent who doesn't seemingly doesn't love them or that loves with their fists uh let's see here let me go on let me look at the last one I think the last one is from the uh yeah the overlook hotel third floor 1958 this is from the mob shooting that takes place and that's the last one uh the overlook was at home with the dead and then it starts with part one prefatory matters so that is this version of the shining and I really really wish oh and we'll we'll talk about after the play also but I really wish that that section if not after the play before the play was in every single version because there's so much awesome stuff in there now as far as the uh deleted chapter from the shining manuscript uh that is the final chapter of the book and it's uh deleted chapter from the shine manuscript way back when it was called the shine chapter 58 newslog uh often referred to as after the play and then it's a bunch of uh newspaper articles dealing with which you know dealing with what happened to each and every person afterwards what happened to uh I almost called Shirley Duvall what happens to Wendy and Danny and what happens to uh Dick Halloran which is very interesting because pieces bits and pieces of that are mentioned in Dr. Sleep but until next time I am Benny you have been you this has been another Stephen King spoiler review I'll talk to you guys later bye bye