Lecture 6

Instructor

Jeremy Jackson

| May 5, 2020

Location:

NW 3428

| New Westminster

Sir Ken Robinson: "Learning happens in the minds and souls, not in the databases of multiple-choice tests"

**Text Font Conventions**

- You will be responsible for knowing a number of definitions of key concepts. You may be asked to give an accurate definition and example of any of the key concepts. Key concepts are in italics, bolded and colored red throughout the notes.Key conceptsCritical points - There are some points that require extra emphasis because they are fundamental to the example or concept being discussed. Critical points are bolded, in italics and colored orange.

- These are the questions given in the learning objectives document.Course learning objective questions

**Lecture 6**

This week, we begin with the methods used in statistics to dress the sampling problem. That is, the problem of how we use sample information to make inferences (fancy word for educated guesses) about the population from which the sample was drawn. To begin the process of understanding this problem, we will need to learn about a new kind of distribution - the sampling distribution. The first steps are to name, define, and provide basic examples of sampling distributions. So let's start with a technical video on sampling distributions.

1) Sampling distributions technical video....here

Now watch a slightly less technical video on one kind of sampling distribution...the sampling distribution of the lowest number. Make sure you have the definition of a sampling distribution next to you as you watch this video. Always go back to the definition and make sure you understand each part of the definition and how it relates to the example.

2) Sampling distribution of the lowest number....here

You should now be able to answer learning objectives question 14 - What is the sampling distribution of the median? Go ahead and write an answer and post it on the discussion board for me to look at if you would like feedback. The next step here is to learn about a very important type of sampling distribution that is central to the statistical methods we use in psychology - the sampling distribution of the mean.

3) The sampling distribution of the mean technical video....here

Now, the features of the sampling distribution of the mean are described by something called the central limit theorem(CLT). Let's take a look at the CLT in a technical video.

4) The central limit theorem technical video....here

In the next two videos, I put together these two ideas into slightly less technical videos on the sampling distribution of the mean. There is a fair bit of overlap between these videos and the previous two technical videos but there is also some additional insight that might be useful to you.

5) The sampling distribution of the mean for samples of size 2 drawn from the population bag....here

6) A quick video on the central limit theorem as it applies to your population bag....here

Now let's take a look at how we use the CLT to calculate the probability of complex events.

7) How we use the CLT....here

By the time you have watched and taken notes on the videos above and worked to understand them and commit to memory the names, definitions and symbols associated with each concept, you should understand what a sampling distribution is, how it differs from sample distributions and population distributions and how the idea is used to calculate the probability of complex events. You should also be able to answer learning objectives question 16 - What is the role of sampling distributions in statistics? Give your own example of this.

That's it for this week. Feel free to ask questions on the "Course Questions" discussion board in Blackboard. I will answer questions on Tuesday thru Thursdays of each week.

Now on to lecture 7.