 Learning Objectives

Psychology 2300 - Introduction to Statistics

Instructor - Dr Jeremy Jackson

QUIZ 1

0) What is the difference between descriptive and inferential statistics?

1) What are we squaring in a sum of squares?

2) Why is the mean more sensitive to extreme scores than the median?

3) Explain why the median rather than the mean is often calculated for "time to event" data.

4) In what sense is a variance a mean?

5) Give an example of a real-world case in which it would be important to report the AAD of a set of scores.

6) Give your own example of a real-world case in which it would be important to report the median as opposed to the mean of a set of scores.

7) If the Pearson r is .1, is there a weak relationship between the two variables?

8) Illustrate why outliers are so important to identify in the correlation/regression context.

9) Using a diagram relate the concept of conditional distribution to correlation.

QUIZ 2 & 3

10) What is the shape of a distribution of standard scores? Explain using a diagram.

11) Why is the AAD a better representation of spread than the SD?

12) What is the difference between the standard deviation and the standard error? Is the standard error a standard deviation?

13) What is the sampling distribution of the median?

14) If the standard deviation of the population is 100, how large must the sample size be to make the standard deviation of the sampling distribution of the mean 50, 25, 20, 10 and 100? (These are example values, I may put in any numbers here).

15) How many standard deviations above the mean of the sampling distribution of the mean is a mean of 12 if a sample of size 16 was drawn from a population with a mean of 0 and a SD of 40? (These are example values, I may put in any numbers here).

16) Describe the difference between a probability distribution and a probability density function.

17) What is the role of sampling distributions in statistics? Give your own example of this.

18) What is the logic of hypothesis testing? Give your own real-world example and associate each part of your example with the associated technical concepts.

19) Is the statistical significance of a finding greater as the p-value goes down?

20) What makes the null hypothesis so important?

21) What is the single most important number in statistics and why is it so important?

Final

22) Why do we not make alpha 0?

23) If the p-value is low, is the probability that the null hypothesis is true also low?

24) If the p-value is not less than alpha, do we accept the null hypothesis?

25) Why is beta more importat than alpha in the real world, but not in the "population bag" example we did in class?

26) What is the difference between correlation and regression?

27) Describe using diagrams how we quantify error in the simple linear regression context. What do we call error in the regression context and how does error relate to the model we are fitting to the data in this context. Be sure to write down the model as well as giving diagrams.

28) Explain how it is that it makes sense to use an analysis of variance to test for differences between means.

29) Give an example of a case in which it might be appropriate to use an ANOVA and provide your own simple data to demonstrate the calculations that are needed to produce a P-Value. Provide the interpretation you would give in the case/example you provided.

30) What is Eta squared? Give your own example to demonstrate what Eta squared measures. be sure to distinguish between variability due to the IV and variability due to subject variables.

31) Distinguish between data analysis, hypothesis testing modelling and estimation. Give a simple example of each and discuss the appropriate contexts in which each should be used. 