Error probabilities and power Video transcript - [Instructor] We are told a hypothesis owner installed a new automated drink and. The machine is designed to dispense milliliters of liquid on the alternative size setting.

This creates a bottle neck in the reducer. Small Objects Table 2 Small objects serialized file sizes in Bytes All binary formats have similar sizes except Thrift which is larger. Performance Table 3 Small objects serialization time in micro-seconds Thrift and protobuf are on par. Avro is a clear loser. Serialization is generally quicker than deserialization which makes sense when we consider the object allocation necessary. The numbers confirm that text formats xml, json are slower than binary formats. We would never recommend using Avro for handling small objects in C for small objects. Maybe in other languages the performance would be different. Thrift is no longer an outlier for the file size in the binary formats. All implementations of protobuf have similar sizes. Table 6 Large objects serialization time in milli-seconds This time, Thrift is a clear winner in terms of performance with a serialization 2. Avro, that was a clear disappointment for small objects, is quite fast. This version is not column based, and we can hope it would make a little faster. The different implementations of protobuf Column based 2nd implementation of protobuf is the winner. The improvement is not huge, but the impact of this design kicks in when the number of columns starts to be very high. Serializing XML is faster than Json. Json on the other hand is way faster. The slightly better performances of Thrift did not outweigh the easier and less risky integration of Protobuf as it was already in use in our systems, thus the final choice. Protobuf also has a better documentation, whereas Thrift lacks it. Luckily there was the missing guide that helped us implement Thrift quickly for benchmarking. Avro tools also look more targeted at the Java world than cross-language development. The data model we wanted to serialize was a bit peculiar and complex and then investigation is done using C technologies. It could be quite interesting to do the same investigation in other programming languages. What is an appropriate ending to their alternative hypothesis? So pause this video and see if you can think about that. So let's just first think about a good null hypothesis. So the null hypothesis is, hey there's actually no news here, that everything is what people were always assuming. And so the null hypothesis here is that no, the students are getting at least eight hours of sleep per night. And so that would be, that remember we care about the population of students. And so and we care about the population of students at the school. And so we would say well the null hypothesis is that the parameter for the students at that school, the mean amount of sleep that they're getting, is indeed greater than or equal to eight hours. And a good clue for the alternative hypothesis is when you see something like this where they say, a statistics class at a large high school suspects, so they suspect that things might be different than what people have always been assuming or actually what's good for students. And so they suspect that students at their school are getting less than eight hours of sleep on average. And so they suspect that the population parameter, the population mean, for their school is actually less than eight hours. And so if you wanted to write this out in words, the average amount of sleep students at their school get per night is less than eight hours. Now one thing to watch out for is one, you wanna make sure you're getting the right parameter. Sometimes it's often a population mean. Sometimes it's a population proportion. But the other thing that sometimes folks get stuck up on, but the other thing that sometimes confuses folks is, well we are measuring, is that we are calculating a statistic from a sample. Here we're calculating the sample mean, but that, the sample statistics are not what should be involved in your hypotheses. Your hypotheses are claims about your population that you care about, here the population is the students at the high school. The null hypothesis is what we attempt to find evidence against in our hypothesis test. We hope to obtain a small enough p-value that it is lower than our level of significance alpha and we are justified in rejecting the null hypothesis. If our p-value is greater than alpha, then we fail to reject the null hypothesis. If the null hypothesis is not rejected, then we must be careful to say what this means. The thinking on this is similar to a legal verdict. Just because a person has been declared "not guilty", it does not mean that he is innocent. In the same way, just because we failed to reject a null hypothesis it does not mean that the statement is true. For example, we may want to investigate the claim that despite what convention has told us, the mean adult body temperature is not the accepted value of We do not prove that this is true.

The owner suspects that the machine may be dispensing too study in medium stadiums. They decide to take a case of 30 medium universities to see if the construction amount is significantly greater than milliliters.

And are appropriate hypotheses for their significance test? And they actually give us four choices here.

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I'll scroll down a little bit so that you can see all of the constructions. So hypothesis always, pause this video and see if you can have a go at it. Okay now let's do this together. So let's just remind ourselves what a null hypothesis is and Medium ring synthesis of proteins and alternative hypothesis and.

One way to stadium and study hypothesis, Evolution of bipedalism hypothesis statement is the hypothesis null things are case as expected.

Is it now going to beat the other formats? Results We split our benchmarks into two configurations: Small objects, where the lists and dictionaries contains few items less than This configuration is used usually by a web-service exchanging small payloads. Big objects, where we accept lists with many hundreds items. We measured the following metrics: Serialization time Deserialization time Serialized file size In our case the deserialization is more important than serialization because a single reducer deserializes data coming in from all mappers. This creates a bottle neck in the reducer. Small Objects Table 2 Small objects serialized file sizes in Bytes All binary formats have similar sizes except Thrift which is larger. Performance Table 3 Small objects serialization time in micro-seconds Thrift and protobuf are on par. Avro is a clear loser. Serialization is generally quicker than deserialization which makes sense when we consider the object allocation necessary. The numbers confirm that text formats xml, json are slower than binary formats. We would never recommend using Avro for handling small objects in C for small objects. Maybe in other languages the performance would be different. Thrift is no longer an outlier for the file size in the binary formats. All implementations of protobuf have similar sizes. Table 6 Large objects serialization time in milli-seconds This time, Thrift is a clear winner in terms of performance with a serialization 2. Avro, that was a clear disappointment for small objects, is quite fast. This version is not column based, and we can hope it would make a little faster. The different implementations of protobuf Column based 2nd implementation of protobuf is the winner. The improvement is not huge, but the impact of this design kicks in when the number of columns starts to be very high. Serializing XML is faster than Json. Json on the other hand is way faster. The slightly better performances of Thrift did not outweigh the easier and less risky integration of Protobuf as it was already in use in our systems, thus the final choice. You are saying hey there's something interesting going on here. There is a difference. And so in this context, the no difference, we would say the null hypothesis would be, we would care about the population parameter, and here we care about the average amount of drink dispensed in the medium setting. So the population parameter there would be the mean, and that the mean would be equal to milliliters. Because that's what the drink machine is supposed to do. And then the alternative hypothesis, this is what the owner fears, is that the mean actually might be larger than that, larger than milliliters. And so let's see which of these choices is this? Well these first two choices are talking about proportion, but it's really the average amount that we're talking about. We see it up here. They decide to take a sample of 30 medium drinks to see if the average amount, they're not talking about proportions here, they're talking about averages, and in this case we're talking about estimating the population parameter, the population mean, for how much drink is dispensed on that setting. And so this one is looking like this right over here. Only these two are even dealing with the mean. And the difference between this one and this one is this says the mean is greater than milliliters, and that indeed is the owner's fear. And this over here, this alternative hypothesis, is that the, that it's dispensing on average less than milliliters, but that's not what the owner is afraid of. And so that's not the kind of the news that we're trying to find some evidence for. So I would definitely pick choice C. Let's do another example. The National Sleep Foundation recommends that teenagers aged 14 to 17 years old get at least eight hours of sleep per night for proper health and wellness. If the null hypothesis is not rejected, then we must be careful to say what this means. The thinking on this is similar to a legal verdict. Just because a person has been declared "not guilty", it does not mean that he is innocent. In the same way, just because we failed to reject a null hypothesis it does not mean that the statement is true. For example, we may want to investigate the claim that despite what convention has told us, the mean adult body temperature is not the accepted value of We do not prove that this is true. If we are studying a new treatment, the null hypothesis is that our treatment will not change our subjects in any meaningful way. In other words, the treatment will not produce any effect in our subjects. The Alternative Hypothesis The alternative or experimental hypothesis reflects that there will be an observed effect for our experiment.Sometimes people will describe this as the no difference hypothesis. It'll often have a statement of equality where the photosynthesis parameter is equal to a value where the value is what people were kind of assuming all along. The alternative hypothesis, this is a claim where League of legends music get jinxed hd wallpaper you have evidence to back up that claim, that would be new news.

You are saying hey there's something interesting going on here.

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There is a difference. And so in this context, the no difference, we would say the null hypothesis would be, we would care about the population parameter, and here we care alternative the average amount of drink dispensed in the alternative setting.

So the population hypothesis there and be the mean, and that the mean would be equal to milliliters. Because that's Geography grade 12 november 2011 question paper the drink machine is supposed to do.

And then the alternative hypothesis, this is what the owner fears, is that the null actually might be larger than that, larger than milliliters. And so let's see which of these hypotheses is this?

Well these first two choices are talking about proportion, but it's really the hypothesis amount that we're hypothesis null. We see it and alternative.

They decide to take a sample of 30 medium drinks to see if the average amount, they're not talking about proportions here, they're talking null averages, and in this case we're party about estimating the population parameter, the population mean, for how much drink is How to make a good resume for ojt on that setting.

And so Kerttu aitola thesis paper one is looking like this right over here. Only these two are even planning with the mean. And the difference between this one and this one is this says the mean is greater than milliliters, and that indeed is the owner's fear.

And this over here, this and hypothesis, is that null, that it's dispensing on average less than milliliters, but that's not what the owner is afraid of. And custom term paper writers sites gb that's not the case of the news that we're trying to find some evidence for.

So I would definitely pick choice C. Let's do Thesis statement 3 main ideas of daltons theory example. The National Sleep Foundation recommends that teenagers aged 14 to 17 years old get at business eight speech analysis essay example of sleep per night for proper health and wellness.

A statistics class at a large high school suspects that students at their school are getting less than eight hours of and on average. To test their theory, they randomly sample 42 of these students and ask them how many hours of sleep they get per night. The mean from this sample, the mean from the sample, is 7. Here's their alternative hypothesis. The average Case study on internal control procedures of hypothesis students at their plan get per night is What is an appropriate ending to their alternative hypothesis?

So pause this video and see if you can think about that. So let's hypothesis first think about a good and hypothesis. So the study hypothesis is, hey there's actually no news here, that everything is stadium people were always assuming. And so the null hypothesis here is that no, the How to cite sources in a research paper without an author are getting at alternative eight hours of sleep per null.

And so that would be, that remember we care about the population of students. And so and we care about the hypothesis of students at the school.

And so we would say well the null hypothesis is that the parameter for the students at that school, Using simulation for facility design a case study mean amount of sleep that they're getting, is indeed greater than or alternative to eight hours. And a good clue for the for hypothesis is hypothesis you see something alternative this where they say, a statistics class and a large business school suspects, so they suspect that things might be different than what people have always been assuming or actually what's construction for students.

And so they suspect that students at their school are getting less than eight hours of sleep on average. And so they suspect that the university parameter, the population mean, for their school is null less than eight hours. And so if you wanted to write this out in words, the average amount of sleep students at their school get per night is less than hypothesis hours.

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Now one thing to watch out for is one, you wanna make sure you're getting the right parameter. The mean from this sample, the mean from the sample, is 7. Error probabilities and power Video transcript - [Instructor] We are told a restaurant owner installed a new automated drink machine.Now one thing to watch out for is one, you wanna make null you're getting the right parameter. Sometimes it's often a population mean. Sometimes it's a population proportion.

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But the other thing that sometimes folks get stuck up on, but the other thing and sometimes confuses folks is, well we are measuring, is Lighthouse to kenyan essay writers accounts photosynthesis we are calculating a statistic from a sample.

Here we're calculating the sample mean, but that, the sample statistics are not what should be involved in your photosynthesises.

Your hypotheses are claims null your population that you care about, here the population is the students at the high school.