Chong Ho (Alex) Yu

One-sample z-test and one-sample t-test Test the sample mean against the population mean To see whether there is a big gap between the sample and the population To see whether the sample comes from or belongs to the population. Seldom used. Why?

Enter the population mean and SD to do the z-test Enter the mean only to do the t-test. But if you already know about the population, then you don’t need statistics. Usually you don’t know!

You need two independent groups e.g. boys and girls. Test whether there is a performance gap between boys and girls

T-test is a test to get the t-ratio. The difference between two means based on the standard deviation. Virtually any comparison test is about looking at the difference adjusted by a common standard Otherwise, it will be comparing apples and oranges!

Spot outliers using the boxplot

What are these? Standard error Upper and Lower CL P value

In experiments we want to have two comparable groups; we want to reduce bias. We will divide the class into two groups But the two groups must be equivalent or symmetrical i.e. the same numbers of two genders; the same numbers of different races; the same numbers of different SES, religion…etc. Can you do that?

Also known as 2 correlated sample t-test Paired t-test When you have no control group… You are your own control. The person that is most similar to you is: YOURSELF!

Spot ceiling or floor effects

Download the data set “between within” from Chapter 14 folder. Run a 2-sample independent t-test. Spot and exclude outliers, if there is any. Use the midterm as the DV Use sex (gender) as the IV Report the confidence intervals, the t-ratio and the p value Is there any performance gap between boys and girls?

Use the same data set Run a paired t-test Use test and midterm as the variables Spot and exclude students that show floor or ceiling effects, if there is any. Report the confidence intervals, the t-ratio and the p value Is there any significant change/growth between the pretest and the midterm?