Learning Objectives

Psychology 2300 - Introduction to Statistics

Instructor - Dr Jeremy Jackson

Potential Short Answer Quiz Questions

Grading Scheme for SA Questions

 Click Here for Example Multiple Choice Questions



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

1) You may be given sets of numbers and asked to quickly calculate the AAD, SD and Variance. For example, I will expect you to be able to calculate the AAD, SD and Variance of the following set of numbers fairly quickly: 3, 3, 4, 5, 5.

2) You will be asked to describe distributions of scores in terms of their location, spread and shape. You should know multiple different ways to describe location (e.g., mode, median, mean), spread (i.e., AAD, var, SD), and shape (i.e., unimodal, skewed to the left, bell shaped, leptokurtic).

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

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

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

6) In what sense is a variance a mean?

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

8) 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.


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

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

11) You will be asked to convert distributions of raw scores in to standard scores very quickly. You will be given the SD or Variance of the distribution and then asked to draw the distribution of raw scores and standard scores - you may have to work out the mean and SD of the distribution for yourself.

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

13) What factors influence the standard deviation of a sampling distribution?

14) What is the sampling distribution of the median?

15) 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).

16) 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).

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

18) When is inferential statistics not relevant?

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


19) 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.

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

21) What makes the null hypothesis so important?

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

23) Why do we not make alpha 0?

24) If we reject the null hypothesis, is it wrong? Why or why not?

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

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

27) If we have more subjects, would we be more likely to reject the null hypothesis?



28) What is the difference between correlation and regression?

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

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

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

32) 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.

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

34) In an ANOVA context, what does a very LOW value of F mean?

35) 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.

36) 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.

37) 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.