Okay. I'm going to go ahead and get started because these 30-minute sessions are relatively short. And the first couple of minutes, I'm going to be doing a lot of, you know, just introducing myself anyway. So, thank you so much for joining today. I am going to talk about what we have until this morning been calling Kubo, and I will probably still call Kubo because otherwise I'll kind of trip over the new words that were announced this morning. So, cloud foundry, container, not engine, runtime. There we go. Container runtime. My name is Cornelia Davis, and I work at Pivotal. I'll tell you more about myself in just a moment. At the moment, my role is that I am driving the go-to-market for Pivotal container service, which is the commercialized version of Kubo. And so, forgive me, I will try to stay in the open source here, but I may, again, trip over that and use the Pivotal container service or PKS name as well. So, let me tell you a little bit more about me. I have been in the industry for a long time. I have the gray hair to show for it about 30 years. Always have been a developer. Didn't come from the operation side of the house at all. And I say wasn't ops because I'll tell you, honestly, when I first started working with cloud foundry, I believed everything everybody said about Paz being all about the developer. And then when I actually started working with customers, because the role that I'm in is really in taking our products out to the customers and then taking kind of these early customers and bringing that back to the product, I realized very, very quickly that platform as a service and now the container service as well is as much an operations product as it is for the developer. So, I know here at CF Summit, we keep talking about the developer, but there's a lot of value on the ops side as well. So, I have learned ops and even paired on our cloud ops team. My background is that I've been working in web architectures for a long time. More than 10 years was one of the first people that talked about restful services back in my EMC days when I worked in the corporate CTO office. And have been really working with cloud native applications for five years in the context of cloud foundry. So, I was still at EMC. It was before the pivotal spin-off. Working on emerging tech in the emerging tech area I was working on was cloud foundry about five years ago, a little more than five years ago. And so, like I said, I work for Pivotal. And more recently, just a little shameless plug, I am working on a book. I'm working with Manning on a book called Cloud Native. And in fact, tomorrow at around lunchtime, we're going to have many versions, the first three chapters of that will be available on our Pivotal booth. All right. So, what I want to talk about today is this is a slide that you may have seen or in fact, my colleagues, Fred Mello and Megan from Google, are presenting on Kubo at the same time. So, it's unfortunate we have two Kubo talks at the same time. And they probably have this slide as well. And the whole point here is that for the last four or five years, we have been running cloud native applications. And that was really one category of workload that existed in your enterprise. But every one of you in the enterprise has workloads that you need to run and that you need to take care of that are not cloud native, right? Anybody running non-cloud native workloads in your environment? And so, the whole point here is that there's different workloads, different types of workloads, and we want to provide the environment for all of those. And that's what the announcement even this morning was about in that cool little snazzy video that we saw at the beginning of the keynotes that showed those different dial tones. Now, you have all of these different workloads, and what we've given you in the past was this. And when I say we, I mean both the Cloud Foundry Foundation as well as Pivotal and various other providers is what we gave you an application platform. And if it didn't fit on the application platform, well, we also gave you IaaS. We gave you something that was at the infrastructure layer, and that's Bosch, right? So Cloud Foundry has actually always been more than the elastic runtime, more than the application service. It's always had Bosch as well. So this is what we used to give you. And now the whole idea is that we are moving, we're shifting around those abstractions. And IaaS is still there under the covers, but we've shifted it around so that we are no longer saying if your workload doesn't run on application dial tone, on C's haiku, here's my code, run it in the cloud for me, I do not care how. If it's not using those abstractions, you had to go all the way to IaaS, we're giving you other abstractions now as well. So containers being the abstraction that we're going to talk about today. So that's the kind of grounding. And what Kubo does is essentially one of the first things that I want to point out is that we've often talked about Kubo being a way that you can say, well, if it doesn't run on the ERT, if it doesn't run in the app platform, that you can run it on Kubo, what I'd like you to do instead is I'd like you to think about it from the perspective of, well, what am I running on infrastructure as a service today or virtualized infrastructure, maybe even bare metal, what am I running down at the infrastructure layer that I could be running on some higher level platform because I get more value out of that. So that's really, it's really about that arrow that I want to talk about today. So okay, we have this container platform, which I'm not going to go over the details of what Kubo is, but it is essentially giving you the ability to get managed Kubernetes. And so we will be talking about some of those values, but it's Bosch managed Kubernetes clusters. So how many Bosch fan folks in the room? A handful of you. So I am definitely one of the biggest Bosch fan girls on the planet. In fact, when I first started working with Cloud Foundry five years ago, I wasn't working with the ERT at all. I was working with Bosch. So one of the first things that I did was I built the very first, very early Bosch release for Gemfire. So this was, like I said, five years ago. It wasn't any good because I'm not a very good programmer anymore because I don't spend enough time cutting code, but it kind of proved the point. So I have been a Bosch fan girl for a very long time. So that's what Kubo is, is this Bosch managed Kubernetes clusters. And again, we'll talk about those things. But okay, I have a Kubernetes cluster. Great. What I want to do today is really focus on the workloads, not focus on the management of the cluster. All of that is great and wonderful. And we'll talk about those values, but it's only really relevant when you start thinking about what are the workloads that I'm running on my Kubernetes clusters and what are the values that I'm getting out of that. So when I started thinking about organizing this talk, and by the way, this is based on customer conversations. So I've been working out there talking with some early customers for the last, I would say, three or four months. This is all reflecting some of those conversations. And when I talk to them about the workloads that they're running, they kind of fall into two different categories. There's code that they, as an organization, are developing. Now we'll talk a little bit more about that in detail, but it's code that they own. So they have the source code in some repository somewhere. And then there's a whole other set of workloads that they want to run in a containerized environment where somebody else owns that code. So specifically, I'm talking about things like an ISV providing software. So MongoDB, CouchDB, Spark, Elasticsearch, those types of things. So I want to talk about those workloads and those two different categories. And let's start with code that you develop. Now if you know Pivotal, you'll know that we like two by twos. So we do a lot of two by twos in a lot of different places. And it actually is a really helpful abstraction, a helpful way of kind of framework for us to organize things for looking at these workloads as well. And so I've got two different axes here. On the one axis, on the vertical axis, what we have is the architectural style of the application. So the workload that I want to run can range anywhere from being fully cloud native, totally stateless, can be totally ephemeral, nodes can come and go, IP addresses and identities don't matter, all of that stuff. And on the opposite end of the spectrum, oh, and microservices, of course, small components, on the opposite end of the spectrum, we have what I'm going to call a traditional workload, which tends to be larger bodies of code. They tend to be more statically deployed, not quite as resilient to changes in the topology, those types of things. On the horizontal axis, I have the frequency with which you're changing that code. So you have code that you're cycling all the time. You've got weekly iterations, you're doing a push into GitHub several times a day. Each time you do a push, it runs some unit tests, and then once a day we run the integration tests, and maybe we're even releasing that software to our users on a weekly basis, or every two weeks, or maybe every day. So we've got the very frequent changes on the far right-hand side, and on the left-hand side, you've got infrequently changing code. So you might see where I'm going with this, because I'm betting that you have a lot of workloads that are traditional workloads that you're not really doing active development on, but they're still providing value and you're still running them in production. Yes? Okay, so we'll talk about each one of those. Now, let me organize what went into each of those categories as I started to organize this talk. So when it comes to fully cloud native with frequent changes, we're really going after developer productivity, as well as operational efficiency. And we're here at Cloud Foundry Summit, right? This is the traditional Cloud Foundry product. So what we're now calling the application runtime. Okay, so the traditional Cloud Foundry setting. No argument there, and I'm not going to talk about that in much detail. On the left-hand side in the upper quadrant, we've got cloud native stuff, so it runs well on Cloud Foundry, but maybe I'm not in active development anymore. This goes back to my earlier point of Cloud Foundry isn't just for the developer, there's all sorts of operational benefits as well. And so there, again, arguably, the elastic runtime, or the application runtime, what we're calling it now, provides a great platform for that operational efficiency. Now, let's talk about that lower left-hand quadrant, which is, hey, I've got code that I'm not cycling a whole lot, and it's more of a traditional architecture. There, what we've done in the past is we've run that on infrastructure directly, and now we're going to be running that on the container runtime. And I'll go into that in more detail. And then the interesting one is in this other side, which is to say I've got a traditional application, let's say. It's not broken down into microservices, but I do want to iterate it a little bit more frequently. Now, we know that from a DevOps perspective, that if I've got large monoliths where I have to do integration testing, maybe I can't release it every day, but it's more frequently than I've done in the past. Maybe I'm releasing it once a month. And there, developer productivity is still important, but there's a little parenthetical remark that says down there that one of the differences, of course, between the application runtime and the container runtime, is that the things that you have to have to support that agility, the developer productivity, you got to bring it yourself. And we'll talk about that more in just a bit. All right, so I'm going to not talk about the elastic runtime anymore, so I'm going to push that off to the side, and we're really just going to talk about these two categories. So let's talk about the lower left-hand quadrant to start. So this is where I'm running traditional code that you have developed with infrequent changes. Now, what are you doing for those infrastructure deployments today? And I'm going to kind of postulate that you do something that looks a little bit like this. The developer writes the code, then you have some kind of an approver that says, all right, this is ready to go for security approval. So it goes to your CSO office. Your CSO office does whatever they do. They maybe do code scans. They do some manual code reviews, those types of things. And they give it their approval, and now it moves on to the compliance office. Now, the compliance office goes ahead and does its compliance, hands it over to QA, and maybe the orders are a little bit different. That's not the point. But you can see here that we have an approval process. And then before I roll this out into production, and by the way, maybe QA is doing that final like performance testing. Before I can roll out it out into production, I also have to involve the change management folks. And so I'm getting approval from the final QA. Yep, it passed those final QA tests. Change management says, yep, everything's cool. And you can deploy that into operations. And this whole process is generally happening as you're deploying into the infrastructure directly. So we've got all of this happening against the infrastructure. So this very long drawn out process, and who has a process that looks roughly like this? You might call it promoting into upper environments. Yep. So it looks something like that. And again, what you're doing is you're generally doing these things as you're doing dev, you're testing in the infrastructure, et cetera, et cetera. So the interesting thing about these workloads when it comes to containerization is that you can, and this is super important, I'm not going to ask you to retool your entire process. For those of you who have been working with Cloud Foundry, and maybe you're even working with Pivotal or some of the other Cloud Foundry related vendors that are here, what we've been doing for the last many years is we tried to get you to change an awful lot. We said, ah, well, you know what? We want you to change your process.  We want you to change all of these different, even your organizational structures. We talked about all of this change, and you've implemented that for a subset of your workloads. But again, there's all of these other workloads. Do I really need to retool everything to just be able to run those? And so what I'm going to suggest to you here, and what we're working with customers to do, is not retool all those processes. We want to take that same process, and instead of doing that process against infrastructure, we're doing that process during the creation of the image. So all of those approvals, so that by the time I get my image at the end, it's approved, and it's ready to go. And now I just need a place to run that, and that's where the container runtime comes in. So that's the first thing to realize is that we're not asking you to retool everything to be able to use this platform. All right. So to summarize that, it's minimal disruption to the current process. And again, the reason we can get away with this is that manual process, or maybe slightly automated process, takes a long time. So this only really can come into play in those things that are infrequently changing. Because if it takes six weeks to go through the process, well, it's going to take six weeks to go through the process to generate the next docker image, right? All right. So then you might ask the question, why do I get any value in doing that? I'm not asking you to change your processes a ton, but I am asking you to do it instead of on infrastructure, do it against the docker image. So I'm asking for some changes. Is it worth it? Well, the answer is, it's absolutely worth it. Because the whole point with PKS, sorry, PKS, container runtime, is that you get all of the benefits, all of the magic that you got from Bosch. So you get health management, logging and scaling. Actually, that comes from Kubernetes. So Kubernetes has some of those capabilities that we've had in the elastic runtime for the last four or five years. Some of those things exist for your workloads on Kubernetes. So you have the ability to have somebody who's watching the health of that, who's doing log aggregation, those types of things. Definitely valuable. Multi-cloud. So when you were doing that against infrastructure, you had to select the infrastructure first. So now you have the ability to create a docker image. That docker image can be run on the container runtime on a number of different clouds. So you have the ability to cloud bursting those types of things. You get kernel and Kubernetes upgrades. So when Kubernetes, and I forget who talked about this, it was in a different meeting yesterday. When Kubernetes has, for example, a vulnerability or some type of update that it does, we will do those rolling upgrades for you. And I'll talk more about this in just a moment. And then there's consolidation. So we are seeing this. We're hearing this from customers that even though they had infrastructure as a service, which gave them some level of consolidation, they didn't need independent physical devices anymore. They could use a single host and support multiple workloads on that with multiple VMs. They're finding that they get even more density on those hosts by going to containers. So those are all the values that you get by moving from an infrastructure direction into containers. But there's a but. And there's almost always a but. And I'm going to speed up a little bit because I'm going to run out of time here. I only have about 10 minutes left and about 20 slides to go. The point is here that the application may not be changing all the time. But the things that are running underneath the application, the Kubernetes version itself, the stem cell that it's running on top of, that might be changing much more rapidly. So can I really roll things like the Kubernetes version without getting in lockstep with the application team? Can I do those, even though maybe I'm releasing every quarter on the application, can I do more frequent releases under the covers? And there, I might be asking you to do something more than you do today. Because it turns out that the real killer feature here is that you need to have a test suite. So I'm going to quickly show you here a couple of animations that I've used in talking about the application runtime over the last several years. What we have here is we have three different environments. These are three different cloud foundry instances or three different orgs or spaces. And I've got my application team, which is now able to just commit code, commit it into the CI environment, so the dev environment. Every once in a while, they do integration tests in the acceptance environment. And when the business says, okay, let's release it to production, they release it to production. And so the cycle goes. So that's what cloud foundry is designed for is to support this very agile developer workflow. Now, the platform team that's responsible for under the covers, so the platform itself stem cell upgrades, handling CVEs, those types of things. What this separation of duties did was the way that it's architected allows the platform team who's dealing with the CVE to say, hey, let me check out that CVE in a staging environment. Here's the important part. Run the smoke tests for my application on that staging environment. If the smoke tests pass, then I can go ahead and deploy in a rolling upgrade fashion, rolling upgrade fashion. That's what allows me to not have to sync with the app teams, roll it out to production. You need to have the same thing. What enables this workflow is you have to enable it, even if you're infrequently changing, if you want the platform team to be doing frequent updates, what we need is we need those smoke tests that the platform team can run in their staging environment before they roll out into production. All right. So that's that lower left hand quadrant. Now, let me talk about the lower right hand quadrant then for a moment. So notice that this is, I've got traditional code that is frequently changing. What that means, therefore, is that I'm frequently changing the image. So remember, I said that in creating that image before, if it took six weeks to go through those approvals, no problem. I could create that and I'm only changing the image every six weeks. Now, I'm frequently changing the image.  I'll tell you honestly, two years ago, at the height of the Docker, Docker, Docker, Docker, Docker, now we're Kubernetes, Kubernetes, Kubernetes, which is a lot harder to say in sequence, but Docker, Docker, Docker, Docker, I actually had people say, I would ask them, well, why do you want to use Docker? And they would say, oh, because the developer can just give me the container and it'll just run. Great.  And they were like, oh, right. Good point. So that's really what I'm talking about here, is this recognition. And nobody says that anymore. Everybody recognizes now what's involved in creating these container images that meet those corporate requirements. But it's now up to you. So for example, on the application runtime on the left-hand side, when you had to come up with an approved base image built into Cloud Foundry, what you need to do now is figure out what your standardized base images are and manage those, manage the accessibility to those. Then what about runtime and other file system dependencies? On the traditional Cloud Foundry side, you have an approved build pack. So your CSO office has gone through and approved that build pack. On the other side, you've got to figure out how to do that yourself. You've got to come up with the pipelines, the docker files, all of those things. You're going to only allow approved images. Well, you're going to have to do things like, on the Cloud Foundry side, you've got identity and access management, which is keeping you from being able to do things that you're not allowed to do. On the right-hand side, you have to have identity and access management. You also might want container scanning, container image scanning, and many, many more things. So the real point here is that, yes, you get these types of things. So why would I want to do that in that lower right-hand corner? Well, the beautiful thing is that you still get all of those values. So it may be worth you going through that work. But this is now no longer the case where I'm saying you don't have to change a ton. I'm saying if you're in that lower right-hand quadrant, you have a lot of work to do. So you really want to look at that and see whether you can push things into the upper right-hand quadrant. Okay. So that takes care of that quadrant. And now I have about five minutes to go. Perfect. So that takes care of the left-hand side there. So let's talk for just a few minutes about the right-hand side, which is the code that someone else develops. What's interesting here is, initially, I didn't think I'd use the same 2x2. But as I got things organized in order to share this with you, I realized that this 2x2 helps as well. I'm going to lay a couple of things over this 2x2. The first thing that I'm going to do is something like Oracle WebLogic. Okay. So it falls into the lower left-hand corner in that Oracle releases this software, I don't know, what are they releasing it in?  So it's infrequently changing. And it's also traditional. And I'll go through what I mean by that. The architectural style is slightly different here. It's similar, but I'll make some slight nuances. Then we have other ISV software like Spark or Elasticsearch that they release software more frequently, for sure, because these are companies that were kind of born in this more agile world. But they're also more cloud-native. And I'll describe what I mean about those. Now, that doesn't necessarily mean that they're so cloud-native that they run on the ERT on the traditional cloud foundry, but we'll talk about that in a little bit of detail. So instead of going along the frequently and infrequently, I'm really just going to look at that spectrum of traditional versus cloud-native. All right. So let's talk about cloud-native. What I mean by cloud-native here is that they are not necessarily independent microservice type of applications where each of the microservices really has a lot of individual autonomy. And you're designing the system so that they can run independently. These are more cloud-native, what I call cloud-native clusters, which is to say that there's multiple components. Even Kubernetes itself has workers and masters and LCDs. So it's a cluster of components. But really, those components are meaningless if they don't have the other components in the cluster. So it's a cloud-native architecture from the perspective that, and in fact, let me go ahead and advance. So from the perspective that it does things like supports nodes, not having a fixed and rigid identity. So when a node goes away and we bring it back, it can have a new identity and it'll add itself back to the cluster with that new identity. That's what I mean by cloud-native there. Or it might be availability zone aware. So we, for example, or the platform makes it availability zone aware so that you're deploying things across and you get some level of high availability that way. And you can have flexible cluster topologies. Arguably some of these traditional software, you can't just add another node. So these are some of the things that define things like spark and elastic search. In terms of data-centric workloads, you need things like persistence. So this completely stateless thing doesn't exist anymore. And I want to point out that no shared storage. And what I mean by that is on the left-hand side here, we see multiple instances of a workload, multiple instances of an app all tied to the same shared storage. That is something that is not available to you in Kubernetes because it really isn't cloud-native. So on the right-hand side, you see here that each instance gets its own data. Well, here's a picture from Elasticsearch where it's now no longer the case that you're handling that data system just in the database. The application is actually handling the replication and the distribution of those things. And maybe even some of the availability zone awareness. So that's what I mean by cloud-native. Now, these things, so why would we run these on Kubo? Well, number one, that you might already have them containerized and all of this on the right-hand side. You get all the goodness of Bosch. So finally, let me talk about the very last thing. And I promise I'm almost done here. Apologize for going, I'll go a couple of minutes over. So let's talk about that other workload, which is Oracle. Oracle WebLogic. So arguably, Oracle WebLogic is not cloud-native. Sure, they're trying to do a little bit more. But Oracle WebLogic is one of those systems where if you lose a node, you have to bring it back with the same identity that it had before. You can't just, it can't take on a new identity. I need to, if I, for example, I've got the storage and I bring back the node and I reattach the persistent volume, great. But if my node has taken on a different identity in the cluster, then everything's broken because it expected that same identity. So what am I talking about here? So let me get very concrete to talk about what do we mean by Oracle WebLogic. So we've got Bosch. We've got the container runtime, which is managing the Kubernetes cluster. And then I'm talking about actually running Oracle WebLogic inside of a Kubernetes cluster. So I have a couple of colleagues that have been experimenting with this and actually have it working. And once you do that, then you just run your normal application inside of Oracle WebLogic server. So going back to this notion of I'm not going to ask you to reach, to retool everything, I'm not going to ask you to make huge code changes, but you can run your stuff on the platform anyway. That's the whole point. Your same application code is still running on WebLogic. Now, you should be asking the question, why? What is the value? Why would I do that? Well, it turns out that all the same values that you saw before. Now, arguably, WebLogic itself does some of this. So for example, they've over the years, they've added some health management. So if a node goes down, bringing the node back up. They've added things like logging. In fact, that's one of the things that they've always done. Maybe allowing you to scale some of those things. But most of the stuff, multi-cloud, can you just do multi-cloud for WebLogic server easily? Nope. Upgrades of the kernel and maybe the Kubernetes cluster underneath? Nope. Consolidation, like we talked about earlier, also very difficult. And one of the interesting things that we found is that there might even be some license consolidation that you can gain. License benefits here because of the way that licensing model is for WebLogic under the covers. So they tend to license on a fairly low level infrastructure abstraction. And so if that's used by multiple instances across the top, you might even be able to save some licensing costs. Now, the difference between that and the elastic search and spark workloads that I used as an example beforehand is that because those things are much more traditional and you have things like persistent identity, you need to leverage something in Kubernetes called stateful sets. And this is taken directly from the documentation there. Stable persistent storage, stable identifiers, ordering. So starting things up in a very specific order. These are the types of things that you needed to do with these traditional applications. I came from documentum. I know very well that the dock base has to start before the dock broker does. So we're familiar with those things. The interesting thing is that stateful sets replace what they used to call pet sets, which I thought was just a great name. So stateful sets don't necessarily mean always state persistence. It just means it's something that you care and feed for a little bit more than you do true cloud native stuff. And it's beta in 1.8. So this kind of summarizes both sides of the spectrum. So code that you write and code that somebody else wrote. And what I want to do is I just want to leave you with a call to action. So Cloud Foundry Foundation announced the kind of the change in organization on this.  Because it's running those workloads that's going to help us tease out. And when I say us, it is the truly the Cloud Foundry Foundation. We have committers on Kubo from Pivotal, from Google, and from VMware are all committing on that open source project at the moment. And the only way that we're going to get that right is by having lots and lots of workloads and finding those edge cases and making sure that we are standing up Kubernetes clusters that can support all those workloads. All right. And with that, oh, and of course, when you do run those workloads, please share with everyone else. And with that, I am at the end of my time. I'm sorry, I almost never leave time for questions. But I'm going to stick around for a little bit. I don't have to rush out right away. And I thank you for your attention.


Hi, I'm Mike Marin. Today we'll discuss the concept of a sampling distribution of the mean. In many fields of study we collect data. These data are used to calculate an estimate, which we use to understand our world. Usually we collect only one random sample and then use it to estimate something, say the mean of a much larger population. But what if we had collected another random sample? Wouldn't the mean be different? Wouldn't our estimate of the population mean change? So how can we know that our one sample can be trusted to represent a much larger population? And how can we know that our work as statisticians is precise? The concept of a sampling distribution helps us to understand how we can use one sample to make statements about the mean of an entire population. A sampling distribution is an important concept in the statistical sciences. It's foundational and it's crucial that you learn it. But what is it really? A sampling distribution is the probability distribution of a given statistic based on a random sample. It describes the what if of all the possible estimates we could have ended up with. Still not sure what I'm talking about? Stick with me. Today to help us understand these concepts, we will be estimating the average length or mean length of fish that live in this lake. The thing we are interested in estimating today, which we will call a parameter, is the population mean. Our variable of interest is the length of a fish. We have our random sample of 25 fish from this lake here, and we will begin to measure the length of each individual fish. Now, in order to get the true population mean of all the fish in this lake, we'd have to measure every single fish. And that could be thousand, and we don't have that kind of time today. So today, I'm going to entrust the skills of a special friend of mine. Neptune, can you please help us calculate the length of every single fish in this lake? That's close! The important thing to learn here is that we must understand how samples behave when we know, well, everything, the truth. We can do this using probability theory. First, we're going to build up an understanding of how samples behave when we know the true values for the entire population. Then, we'll be able to learn how to use a sample to try and make statements about the entire population in the real world, when we don't actually know the truth without help from Neptune. We do this using statistical inference. So, we now know the mean length and the standard deviation of the length. And this probability distribution shows the lengths of all fish in this lake, every single fish. So, we know that this population has a mean length of 40 centimeters and a standard deviation of 10 centimeters. Thanks for your help, Neptune! We now know what we can do when we have the power of Neptune at our disposal, but what if we don't? What if we're back to being regular statisticians? What if we can't count each and every fish? We will now collect a random sample of 25 fish and measure their length and calculate the mean of the 25 length. We will call this our sample mean, or estimate of the population mean. It's important to remember that this sample mean is just one of many we could have gotten just by chance. We would have a different sample mean each time we collect data, because each set of fish is slightly different from one another. Look at this histogram. It's starting to show not 1, not 2, not 3, not 4, but all of the possible sample means we could have ended up with. Wouldn't it be great if we could see a histogram of all the possible sample means without having to collect sets of 25 fish over and over again forever? What would happen if we ended up with a different sample of 25 fish? You'll be pleased to hear that there is a way to know approximately what the shape of this distribution would look like without repeating the sampling process thousands of times. Thanks to Neptune, we now know that the true mean of fish in the lake is 40 cm and the standard deviation is 10 cm. And we know that the distribution of the lengths of all fish looks like this. Then what's going to happen is we're going to go out with our fishermen and we're going to take a random sample of 25 fish from this population. And when we take that sample of 25 fish, we expect our sample mean to be equal to 40 cm, the true population mean. Although, we know that it's not going to be exactly equal to 40 due to what we call sampling variability. Now imagine all the thousands of random samples we could have taken. Each mean deviates slightly because not all samples have a mean that equals Neptune's true mean of 40. The standard deviation of all possible means is called the standard error. And we can work that out. The standard error is the standard deviation of the lengths of individual fish divided by the square root of the sample size. So our standard error is 2 cm. And this tells us, typically, how far the mean length of a sample of 25 fish deviates from the true mean. So typically, we're going to end up with a sample mean about 2 cm away from the true mean of 40. Even without Neptune, we have an idea of how close our estimate will be to the true value. The sampling distribution of the mean will be approximately normally distributed under certain conditions. In fact, an old rule of thumb tells us that approximately 95% of sample means we could get will stay within about 2 standard errors of the true mean, and be between 36 and 44 cm. That's most of them. Not bad for a simple guy with only a paper cutout of a fisherman to help him out. Now, check this out. The sampling distribution, the distribution of all the possible means we could get, is approximately normal or belching. OK, so we think of the sample mean that we're going to get as one of many possible sample means we could get. And we can think of pulling it out of this distribution here. Even if this here, the population of all individual fish lengths is not normally distributed. Like in a different lake, with an unusual proportion of large sea creatures. This here, the set of all possible means, would still be normal in bell shape, as long as our sample size is large. Now that's pretty cool. Now, you may be thinking to yourself, why is Mike spending so much time pretending and talking about what ifs today? Well, the reason is that in everyday life, we generally don't know the true values for an entire population, and we must use a sample to try and make statements about the population we're sampling from. Learning how samples behave when we do know the true values for the entire population allows us to build the theory necessary to make statements about a population when we do not know the true values for the population. In the videos to follow, we will discuss how the sampling distribution of the mean can be used to help us make statements about a population, as we learn how to construct a confidence interval and test claims using hypothesis tests. Don't forget to check out the statistics visualizations that accompany this video. You can find the link in the description. Thanks for watching!


Now, as gunshots echo across the windswept snow-covered reaches of the wild Northwest, Quaker Pop Wheat and Quaker Pop Rice, the breakfast cereal shot from guns, present the challenge of the Yukon. It's Yukon King, swiftness and strongest lead dog of the Northwest, lazing the trail for Sergeant Preston of the Northwest Monad Police in his relentless pursuit of lawbreakers. On King Von Husky! Gold, gold discovered in the Yukon, a stampede to the Klondike in the wild race for riches, back to the days of the gold rush. With Quaker Pop Wheat and Quaker Pop Rice bringing you the adventures of Sergeant Preston and his wonder dog Yukon King as they meet the challenge of the Yukon. Here's my idea of someone who's really smart. It's the person who sees to it that there's a supply of Quaker Pop Rice or wheat on hand at all times. These ready-to-serve cereals hit the spot morning, noon or night. They taste so swell because they're shot from guns. Exploded up to eight times normal size to make them crisp and tender. Wheat or rice shot from guns is nourishing, too. Makes a thrifty, easy-to-fix deluxe family breakfast with milk and fruit. Yes, here's a treat you'll want to repeat. That's Quaker Pop Rice and Quaker Pop Wheat. It was nearly midnight when Sergeant Preston shouted to command the King. G-King! The great dog turned to the right and led the team up the creek bank and into the little town of Gold Flats. King was working as a loose lead, breaking trails into freshly fallen snow. And suddenly he swerved aside and headed straight for a warehouse on the left side of the street. The team followed as a member of force. What's the matter, boy? Hooray, hooray! The dog stopped just short of a white mound in the shadow of the building, and whined nervously as the Sergeant ran up to join him. Something here you want me to look at, boy? He saw at once that it was the figure of a man, and after he had rushed away the snow. His dead King has been shot. Shot in the back. Well, put him on the sled and take him to Jim's cabin. The Sergeant always stayed with old Jim Hanley while he was in Gold Flats, and a few minutes later they reached his cabin near the road of the phase that lined the main street. Hooray, hooray, hooray! S-U, Sergeant? Yes, Jim? What have you got on the sled? A man, a dead man. Jump into your harsophage. I'll give you a hand, Sergeant. I'll close the door. Bring that lamp over, Jim. I got it right here. You know him? I sure do. It's Matt Barrett. This is a harsh thing to say about anyone, Sergeant, but it's good riddance. Why do you say that? He was a bully for one thing and for another. Well, you've come here to investigate that express company robbery. Yes. I'd had to point him out as one of the men we suspect. Who are the others? Jeff Martin and the men who hang around with him. Jeff and Matt were partners in all kinds of government. But there had been a falling out between them? Not only could there been the wars, they had a knock-down drag-out fight at the Monte Carlo last night. Jeff got the worst of it, and he swore he'd get even. Then we must suspect him of murder as well as robbery. Looks like it to me. But this is one time a bullet did some good. Crime's been committed, Jim. It's my job to see the criminals punished. Well, I wish you luck, Sergeant. You'll find that Jeff is a tough customer, though, and smart. He covers his tracks. Snow covered the killer's tracks. How long ago did it stop snowing here? About two hours. The shot was fired sometime before that, and not very long before. There was only a thin layer of snow covering the body. I don't suppose anyone could have heard the shot if it was blowing as hard here as it was on the trail. It was blowing all right, Sergeant. A little before 10 o'clock. Well, first thing to do is find out where Jeff Martin was then. It's early. If he's in town, most likely you'll find him in one of the cafes. I'll take a look. Let's go, King. The Sergeant King left the cabin, and a few minutes later, they found Jeff in the back room of the Monte Carlo. There were several hard-faced men sitting around the table with him. The Sergeant ordered them out of the room, and then... Well, Sergeant, you look mighty serious. I am. Somebody's been suggesting that I had something to do with the Express Company robberies, that is. That's part of it. Well, they're all wrong. I wasn't even in town the night it took place. But you were in town tonight. Where? Between 9.30 and 10 o'clock. I haven't left this table. You can ask any of the boys. What happened between 9.30 and 10? A man was murdered. Say, that's bad. The man's name was Matt Barrett. Matt's been killed? Well, Sergeant, that's more than bad. It's terrible. Matt was just about the best friend I had in the world. Oh?  You mean that little fight we had in here? I didn't mean a thing, Sergeant. You were beaten up, and you swore you'd get even with me. Well, I may have said something like that, but I wasn't serious. Matt was a pal, a partner. Believe me, Sergeant. I sure hope you find the man who killed him. I'll do my best. Sure. And I'll do everything I can to help you. Just don't try to leave town, Jeff. Of course not. I want to help. You can start by sending in those men who were here. All of them? All of them. One at a time. Sure thing. The sergeant spent the next two hours in questioning all the men in the café. The few who'd been playing cards with Jeff swore that he'd never left the back room. From the others, he learned that Matt had been in the café shortly before he was killed. But there was no evidence to establish the identity of the killer. Matt was buried the following morning. And afterwards, as they ate their midday meal, the sergeant and Jim discussed the case. It was Jeff, Sergeant. If he didn't do it himself, he hired someone to do it. You said he was smart. I can't arrest him, Jim. His alibi is too good. Yeah, those friends of his would swear to anything. Now there's an outside door in that back room in the morning call-o. He could have slipped out for a few minutes. Yes, I know, Jim. But then we come to another question. How'd he know where to find Matt? And what was Matt doing on the edge of town during a blizzard? Was he leaving town? Was he... Was Matt a king? Does he want to go out? No. I think there's someone at the door. Well, nobody not. I'll see. Hey, Marie, come back. Did you want to see me? No, I... It is better if I do not. What kind of talk is that? Now come on in here. I will. But it is a sojourn, Mr. Dubtu. Well, he's right here. Sergeant, this is Marie Fontaine. How do you do? Bonjour, monsieur. Now sit down, Marie, and make yourself comfortable. Merci. You seem to be worried about something. Oh, I am, monsieur. I'm afraid. Well, you don't have to be afraid of the sergeant. It is about my husband. Has something happened to him? He is... No. I... I do not know what to say. Well, Marie, just tell the sergeant whatever it is that's troubling you. It's his job to help people. That's true, Marie. But Pierre made me promise to say nothing. And yet I feel that he is wrong. It is not right to know such things and keep them a secret. To know what things? My husband is the watchman at the warehouse. Marie, it's something about last night. Something about the murder. You know who did it. No, monsieur. I do not. But your husband does. He has gone away. He is afraid he will be killed if it is known that he knows.  Marie, if you have any information at all, it's your duty to tell me. I'm an officer of the law and I'm here to protect you. That is what I believe, monsieur. Tell me then. Pierre was in the warehouse when the shop was fired. Yes. Even above the noise of the storm he heard it and he ran to the door and opened it. He saw a man lying in the snow and kneeling beside him is another man. He lights a match and for a second Pierre can see his face. It is a short dam only and the match is blown out. Who was that man? I do not know. Pierre knew him? We... he would not tell me who he was, though. Go on. What else did he say? The man disappeared in the storm. Pierre went to the man lying in the snow. He was dead. Pierre locked the warehouse and came home to me. He was afraid that if he said nothing of what he saw, the police might arrest him for the murder. And if he told the truth, the other man might kill him. It seemed to him best that he go away. Where, Marie? Where is he gone? 240 miles. You're sure? We... I was to stay here and find out what happened. Then I was to join him there. What does Pierre look like? Well, he's kind of small, Sergeant. Dark curly hair and mustache. Pierre Fontaine. That's right. What was he wearing, Marie? The clothes for the jail. What kind of a parka? It is made of deer skin. We'll find him and we'll bring him back. Don't say anything about this to anyone, Marie. I mean, no. And don't worry. No one's going to hurt your husband. Please. Please let it be so, Mr. Let's go, King. We're hitting the trail. We'll continue our story in just a moment. You know, big game hunters may go for a special high-powered rifle when it comes to shooting elephants or lions or tigers. But say, let me tell you about another kind of gun that everyone goes for. It's the gun that shoots Quakerpuff wheat and Quakerpuff rice. Yes, sirree. When you have a big day ahead, long hours at school or playing football or games, you need to stow away a good breakfast. So tomorrow, start the day by eating a heaping bowl full of Quakerpuff wheat or rice with plenty of milk and fruit. These nutritious, delicious, ready-to-serve cereals furnish extra food values, restored natural grain amounts of vitamin B1, niacin, and dion. What's more, they're really keen tasting. They're shot from guns to make them crisp and tender. Yes, these giant size, king-size grains of premium wheat or rice are actually exploded up to eight times normal size to make them bigger and better tasting. That's why Quakerpuff rice and wheat are so popular with so many he-man Hollywood movie stars. Ask mom for wheat or rice shot from guns. Remember, it's never sold in bags or bulk. Always buy the famous big Quaker red and blue package to get the original crisp, fresh Quakerpuff wheat and Quakerpuff rice. Now to continue our story. A few minutes after Sergeant Preston heard the end of Marie's story, the dog team was harnessed and he was driving out of town in the direction of 40 miles. Lefty Wade, one of Jeff Martin's cronies, saw him leave. He saw Marie and Jim standing in the doorway of Jim's cabin. And a few minutes later, he stopped the woman as she walked down the street toward her own cabin. Howdy, Marie. Where's the Sergeant gone? I think it is the direction of Dawson, he takes. Oh, you're wrong. He's heading north. Oh, that is true. He seems to be in a big hurry. Maybe. You must excuse me. I am any big boy. Well, I'll just walk along with you. Have a talk with Pierre. No, you cannot. Why? He is not at home. Well, I'll come along and wait for him. We'll do you no good. He is out of town. Didn't think I'd seen him around today. He is not here. Excuse him, war. Yes, sure. Lefty Watson Marie as she walked down, down the street. Then he headed for the Monte Carlo where he found Jeff standing at the bar. Hey, Jeff, let's sit down in the bag. It's important. Okay. This is all right. Sit down. What are you so nervous about? I just saw the money drive out of town. Yeah, you mean for good? I don't think it's for good at all. Marie Fontaine was over Jim Henley's cabin where he's been staying. Whatever. Maybe she gave him a tip. About what? Marie doesn't know anything. Pierre is a watchman at the warehouse. He must have been there last night. I tell you, nobody saw me. But how can you be sure, Jeff? Pierre isn't at home. He's out of town. Then he wasn't at the warehouse. How can he see me? He was here all day yesterday. And I want to know why he cleared out. Doesn't look good to me, Jeff. If Pierre saw you and the money's gone after him, then you'd better... But it would. Well... There's no way, I suppose, huh? It might be safer. I decided that for myself. All right. It's your funeral. I'm going to find out how the cards lay before I throw in my handy. Now you get Marie and take her up to Old Town. Huh? You heard me. What do you want with her up there? I want to find out how much she knows. She won't come with me. You can persuade her. Take her to the old dance hall. The boys and I will be waiting for you there. Okay. With King breaking the trail, Sergeant Preston made the trip to Forty Mile in a little over two hours. It took him another hour to find Pierre and persuade the Frenchman that he must return to Gold Flats and tell the truth about what he had seen. Pierre, decent law-abiding citizens like you will never be safe unless you do everything you can to put criminals where they belong. But no, Sergeant. I think of Marie. Who was the man you saw, Pierre? Tell me and I'll arrest him. He'll go to jail, he'll be tried for murder, and he'll be convicted. Who was it? It was... it was Jack Martin. And you'll testify to that in court? I would tell the truth about what I saw. Good. We will stop the Gold Flats since the morning. We'll start this minute. It will soon be night. King knows the trail, don't you boy. You'll get us there fast, won't you? Come on, Pierre. The early dusk of the North land overtook them on the trail a few miles outside of Forty Mile. But then the moon rose, and the northern lights sweeping across the sky made the night light day. The miles flew by. But when the Flats were reached, the Sergeant called the team to halt in front of Pierre's cabin. Is the cabin is dark? She is not here. Make sure, Pierre. She may be waiting in Jim's place. We... Marie? Marie? No, she is not here. Hold on, then. We'll plant her at Jim's. I hope so. All set? We... On gate! On! There was a light in Jim's window, but there was no answer to the Sergeant's knock. The door was unlocked, however, and when he and Pierre and King entered the cabin, they saw a note propped up by the oil lamp on the tail. The paper does not say? Yes, it does. Who's Lefty Wade, Pierre? He's a friend of Geoff Martin's. I remember now. He's one of the men who said that Geoff didn't leave the back room at the Monte Carlo last night. They're all crooks. What is it you're afraid about, Lefty Wade? Bad news, listen. So, Marie leaving town with Lefty Wade heading for the hills, I'm following them. The hills? They have taken her prisoner. What's in the hills? Where were they going? There's nothing but the old town. No one lives here now. It's a place where Geoff Martin might go to. Your wife doesn't know it was Geoff who shot Matt. It does not matter.  I don't think so, Pierre. Not when they realize she knows so little. I'm going after her. And I. My two must come. No, Pierre. You must stay here. As soon as I leave, bar the door and don't let anyone in until I get back. My wife. You will promise to bring her back? I promise. All right, King. It was at just about that moment when Jim Hanley reached the old town. The place seemed to be completely deserted. But the tracks of the sled he had been following led to the dance hall. When his gun ready, Jim drove directly up to it. He climbed the steps cautiously and crossed the wide veranda. Only a few short years ago, this building had been the center of a boom town. Now it was a ghostly shell. Not a light shown anywhere. And yet Jim knew that somewhere, somewhere close, he tried the door. He had opened. He stepped inside. It was too dark for him to see anything. He struck a match. All right, Ben. Get it. No, you don't.  No, yes. I've got it. What do we do with him? We've got some light now. Sure. Light the lamp. Where's Marie? What have you done with her? Don't worry about yourself. What's the idea of following me up here? Where is she? She's where you and nobody else will find her. Not until we get good and ready to let her go. Tighten up those knots, Joe. Crooks the lord here. I don't mind just saying it, as long as you can't prove it. If we keep Marie here to make sure Pierre doesn't talk, the sergeant's little trip to Forty Mile won't do him any good. You'll make her tell you. That is easy. She doesn't like to be heard. Well, you can't win. You're going to jail, every one of you. For what? For robbery and murder. Isn't he taught enough there? Read him. I want him to. I suppose you've left worried where you were going. Yes, I did. And it won't be long before the sergeant gets here. We'll be waiting for him. Joe, you and Pete stand guard out in front. You'll be able to see him coming up the trail just like you saw Jim. Now take your rifles and let him have it. But when the sergeant reached a point on the trail where he could see the ghost town, he realized that to show himself on the open slope below would make him a perfect target. He stopped the team. Okay, hold on. King was relieved. His sure instinct told him there was danger lurking in the silent buildings. He also knew that such a threat would never stop his master when there was work to be done. But this stop meant they would proceed with caution. That's too bright to keep on going this way, King. There was another way, though. Half a mile to the east, a wooded draw led to the top of the ridge. The ridge itself was wooded, and it was possible to travel along it until a point was reached just above the town. The back way looks better to me, boy. The sergeant left the team behind and started for the draw with King. It was hard forcing their way through the tangled underbrush to the top of the draw, and the climb took them nearly half an hour. Travel was easier on the top of the ridge. But here, the firs grew close together, and neither the moon nor the aurora could penetrate the darkness. Hard to see, but we'd be sure they can't see us. At last, a point was reached directly above and behind the dance hall. The sergeant started down toward it, but unaccountably, King held back. No heart. Something wrong, boy? King had found a freshly made trail, and he felt that the sergeant should investigate it. I asked him footprints. He looked down, examine them closely with the aid of a shielded match. Some of these were made by a woman, and they headed up the slope. All right, King, let's follow it. Go on. King started up with the sergeant directly behind him. A few minutes later, a glimmer of light showed to the trees. The cabin was so completely hidden by the trees that it couldn't be seen more than 20 feet away. Silently, the sergeant and King advanced toward the single window. The sergeant looked inside. The faint beam of a candle lit the face of a man who was dozing as he sat with his feet propped on the table. This is going to be easy, King. A dark form could be seen lying on the floor. Let's hope we're not too late. The sergeant threw open the door of the cabin. Up with your hands. I'll take your gun. What's the idea? You're under arrest in the name of the Queen. You can't arrest me. If you don't think so, these handcuffs should prove it. I haven't done anything. We'll see what Mrs. Fontaine has to say about that. I'll take this guy out first, ma'am. Oh, thank you. Now I'll get your ropes. Every minute they were going to kill me. Have you seen Jim? He was coming up the slope toward the town. They were waiting for him. They took me away before he got there. I do not know what they did to him, but I have had no shooting. I'll help you up. Where was it they were waiting for Jim? In the dance hall. Jeff then left his own feet. This is the one they called Butch. I'll tie him up before I leave. You'll have to wait here until I get back, Marie. You go to the dance hall? Yes. But it would be four against one. Four against two, you're forgetting King. Well, try not to let them know we're coming. There is something you should see here. Under the table, Sergeant. There is a box. I saw it as I was lying on the floor. It is the kind of box the express company uses to ship gold. I have seen it many times. There's nothing in that box. Which means there must be. There. There it is, Sergeant. Good work, Marie. This is the gold, they stole. I didn't have anything to do with it. But Matt Barrett did. That's right. And he wasn't satisfied with his share. It wasn't that. He wanted to leave town. He wanted his split, and Jeff said no. He started up here after. All the gold's there, Sergeant. Jeff wouldn't let us touch an ounce of it. So you didn't have anything to do with it? I didn't. I was up here on the night of the robbery. You can explain that to the judge. Now I'm going to tie your feet and gag you. In a few minutes Butch was lying helpless on the floor. The Sergeant and King retraced their steps down the wooded slope of the ridge. The rear of the dance hall was in deep shadow from the trees. And it was impossible for the Sergeant to see if there were any guards posted there. But from King's reactions, he knew the coast must be clear. The rear door was ajar, sighing on his hinges. The Sergeant opened it a little more. The rusty hinges protested. The Sergeant King stopped. But the sound evidently hadn't been heard. Then they stepped inside into a narrow hallway. Ten feet from the door, a stairway led to the second floor. As the Sergeant and King were passing it, they heard voices. I could hear something out in the back. Take a look with me. Okay. Swiftly, the Sergeant and King climbed the stairs. Around the turn at the top, a closed door barred their way and they waited. Steps echoed in the hallway below them. Just the door is broken. The wind blew it over. The Sergeant opened the door at the head of the stairs and he and King entered a small room. A stove glowed in one corner. The Sergeant could see an old desk and oil lamp standing on it and two broken down chairs. With his hand on King's head, he started for the other door. The great dog understood the need for quiet. The door opened onto a balcony that overlooked the large central room of the dance hall. A faint light from a lamp turned very low, left most of the room in complete darkness. Then the front door opened and a man was silhouetted against the moonlight.  All right. If necessary, he'll get back there. No need for me to stay down here. We'll meet everybody. You can call me when you see him. I'm going up to the office and get more. The Sergeant and King slipped back into the office and closed the door. A moment later, Lefty reached the top of the stairs leading up to the balcony from the dance hall. He walked along the balcony to the office and opened the door. The Sergeant and King were hidden by it. Lefty started toward the stove. For the odds four to one against him, this was no time for the Sergeant to use gentle methods and he brought the butt of his gun down hard. The Sergeant caught Lefty's sagging body and lowered it to the floor. Then he snapped his extra pair of handcuffs around the man's wrists. Boy, that's only one of them. The Sergeant rose and turned toward the door. King couldn't help whimpering. There was a man outside on the balcony. Jeff had come up the stairs after Lefty. It's a matter of King. The door opened. You're covered, Jeff. You're under arrest in the name of the Queen. You won't get me! After him, King! Jeff ran through the stairs with King and the Sergeant following. But he missed his footing on the first step and started falling. As Jeff yelled, Joe and Pete charged into the dance hall. Jeff, what's the matter? Don't move you two, you're covered. Sergeant Preston. Lock your guns around. Shoot. You've got us. Stay right where you are. You can't see me, but I can see you. One move and I shoot. Okay. The Sergeant descended the stairs. At the bottom, he stepped over Jeff's still form. King was standing guard beside him. The Sergeant found the table.  Who's that lying in the corner? Jim Hanway. Untie him.  Yeah, sure. A few minutes later, Jim was free and was holding a gun on Joe and Pete. The Sergeant knelt over Jeff. I don't know where Marie is, Sergeant. She's in the cabin up above here. Yes. The man who was guarding her was bound and gagged. You hear that, boys?  Are you men still saying that Jeff did not leave the cafe last night? Well, uh, well, we didn't... Butch has already confessed. If you try to protect Jeff, you'll be a party to his crimes. No, no. We didn't have anything to do with it. He went after Matt all by himself. That's better. How about Jeff, Sergeant? Is he dead? No, Jim just knocked out. He'll live to answer for what he's done. You don't have to worry about him anymore, King. The case is closed. In just a moment, Sergeant Preston will give you a preview of Friday's program. Hey, here's something new and different, too. Hurry, start collecting colorful college penance or flags of your favorite big-time football teams or schools. Yes, now you can get authentic flags of famous colleges like Notre Dame, California, Army, and Pennsylvania. A special new series of 48 swell color cutouts are out just in time for the football season. You'll get them only with Quakerpuff wheat and Quakerpuff rice. The swell-tasting breakfast cereals shot from guns. These college penance or flags are right on the backs of packages of Quakerpuff wheat and rice now at your grocers. They're yours at no extra cost. Don't miss out. Don't miss out either on the big news. You will hear it soon on this very program. Tell your pals to listen. You're going to have a chance to get a dream prize of a lifetime. So don't be left out. These radio dramas, a feature of the challenge of the Yukon Incorporated, are created and produced by George W. Trendall, directed by Fred Flower Day, and supervised by Charles D. Livingston. The part of Sergeant Preston is played by Paul Sutton. They are brought to you every Monday, Wednesday, and Friday at the same time by Quakerpuff wheat and Quakerpuff rice. The breakfast cereals shot from guns. Listen, Friday, when Sergeant Preston and Yukon King meet the challenge of the Yukon in the adventure of Find the Body. We have another exciting story for you on Friday. It's about one of my pals, another Mountie, who disappeared. Inspector Conrad was sure he'd been murdered. Without any facts, I was given the assignment of solving the case. When King and I hit the trail, we headed straight into trouble. It was nip and tuck from then on. The story you won't want to miss. Be sure to hear this exciting story Friday. Till then, this is J. Michael wishing you good-bye, good luck, and good health from Quakerpuff wheat and Quakerpuff rice. So long. For a delicious hot breakfast, eat Quakeroats. The giant of the cereals is Quakeroats. Yes, the giant of the cereals is Quakeroats. Delicious, nutritious, makes you feel ambitious. The giant of the cereals is Quakeroats. Say, boys and girls, do you want to be a star someday in sports and activities? Then start on good Quakeroats breakfast tomorrow, because nourishing oatmeal gives you more growth and more endurance than any other whole-grain cereal. Still costs less...


Life totally rebounded on the driest desert on Earth. This might mean that potential life may exist on really dry areas on Mars too. This is your Psypod. At a common desert in South America where it will go decades without rain, scientists found life in the form of microorganisms in the soil. Researchers found several indigenous species of microbial life that will go dormant for decades without water. But then when it rains, they will be reactivated and begin reproducing. They did this by making multiple return trips to the desert in 2016 and again in 2017 and found that the samples they collected to be the same microbial communities in the soil that they observed to have gone dormant when there was lack of water. Lead planetary scientist for this study, Dirk Schultz-Makowicz said, we believe these microbial communities can lay dormant for hundreds or even thousands of years in conditions very similar to what you would find on a planet like Mars and then come back to life when it rains. Right now, we know that Mars has frozen water and studies show high chances of there being snowfall and moisture near the surface. And billions of years ago, Mars used to have small oceans and lakes where early life forms may have developed. But as it dried up and cooled down, it might be possible that microbes may have gone dormant but can spring up again upon moisture. Just as the microbes at the Atacama Desert also did. Future research includes visiting Don Juan Pond in Antarctica to collect samples at very shallow lakes that contain such high salt content that it actually remains liquid even at minus 50 degrees Celsius. The goal is to understand how these life forms manage to survive at the most extreme climates on Earth and those similar to Mars. So we know what to look for when exploring the next possible home base for humanity. Thanks for watching. Don't forget to like and subscribe at TMRO Science which comes up at 200 UTC time every four weeks on Saturday. Thanks guys and keep learning.


Germany will be the main country defending Europe from Russian invasion. In the conditions of the war that Russia is waging against Ukraine, Germany is beginning to understand that it must be a support for deterring aggression in Europe. And the Bundeswehr is rethinking its role. German Defence Minister Boris Pistorius and Bundeswehr Inspector General Karsten Breher wrote about this in an article for the Tagess-Spiegel newspaper. War has returned to Europe. Germany and its allies are once again forced to confront a military threat. The international order is under attack in Europe and around the world. We are going through a turning point, the publication says. It is noted that this fundamentally changes the role of Germany and the Bundeswehr. Today, Germany bears responsibility as the most populous and economically strong country in Europe. Therefore, the article emphasizes, it should be a pillar of deterrence and collective defence in Europe. Our population, as well as our partners, expect us to take on this responsibility, the authors write. For this, they note, leadership, defence capability and a realistic view of geopolitical challenges are needed. The Bundeswehr is ready for war. Its leaders are sure. In particular, an important signal of strength and deterrence is that starting next year, for the first time in history, a combat brigade numbering 5,000 soldiers will be stationed in Lithuania on a permanent basis. As emphasized, the war in Europe, as well as the Hamas attack on Israel, confirm the need for a quick and effective contribution to the international settlement of crisis. It follows on from all this, that the Bundeswehr must be combat ready. Only then can it be a reliable deterrent. This means sufficient personnel, better equipment and structures that meet the new challenges Pistorius and Brea point out. They also emphasize the need to attract new partners to cooperation based on a realistic pragmatic approach, but always with a clear compass of interests and values. This applies in particular to the countries of the Indo-Pacific region, as well as Africa.