Review Outline for Exam 1 (Psy340)

Remember to use the online study guide at the Aron & Aron website as a resource for practice problems, flash cards, etc.! See the link on your 340 homepage!

General Info: There are 3 sections on the exam – short answer (conceptual & definitional questions); calculation problems, and SPSS output interpretation. You may use a calculator during the exam – either bring in your own or use the one on the computer. I will provide a formula sheet (you do not need to memorize formulas) and the normal curve table (if applicable).

Chapter 1

  • Interpreting histograms and frequency polygons (in terms of skewness, number of modes, etc.)
  • Unimodal, bimodal, multimodal distributions – what do they look like?
  • Positively and negatively skewed distributions – what do they look like?

Chapter 2 – Central Tendency and Variability

  • The mean – its interpretation, how to calculate it
  • The mode – its interpretation and how to find it
  • The median – its interpretation and how to find it (odd versus even number of scores)
  • Variance – what is the concept of variance? Be familiar with how to calculate it (you will be given the formula)
  • Standard deviation – what is the concept/interpretation of standard deviation? Be familiar with how to find it (you will be given the formula). How is it related to variance?

SPSS Output – be familiar with the typical output for central tendency and variability (descriptive stats) given in SPSS. Nothing tricky here – just be able to look at output and find the reported mean, standard deviation, etc.

Chapter 3 – Z scores, Normal Curve, Sample/Pop, Probability

  • What are z scores? What information do they provide? How can they be used to make comparisons among scores from same/different distributions?
  • How do you find a z score from a raw score (will be given the formula, just be able to calculate it).
  • How do you find a raw score if given a z score (will be given the formula).
  • What are the mean and standard deviation of z scores?
  • Be able to draw a normal distribution and know the 50%-34%-14% rule (with the remaining 2% in the two tails of the distribution).
  • Use the normal curve table to determine the percentage of scores between the mean and a particular z score (shown in table), or above the z score
  • Use the normal curve table to find a z score associated with a certain percentage of the distribution.

Chapter 4 – Intro to Hypothesis Testing

  • 5 steps of hypothesis testing:
  • 1) State the question as a research hypothesis (what you predict will happen if there is an effect) and a null hypothesis (what happens if there is no effect). The null hypothesis should be the opposite of the research hypothesis.
  • 2) Determine characteristics of the comparison distribution. (What does the distribution look like if the null hypothesis is true?).
  • 3) Determine the cutoff score in the comparison distribution at which the null hypothesis will be rejected. This is sometimes called the critical value and provides a critical region of the distribution. We usually choose a cutoff score above which 5% of the distribution lies – this is ‘level of significance’ and is why we usually talk about “p < .05”.
  • 4) Determine your sample’s score on the comparison distribution. Does it fall within the critical region?
  • 5) Determine whether to reject the null hypothesis. If the sample score falls within the critical region  reject the null hypothesis. This means the research hypothesis is supported and you have found evidence for your research hypothesis.
  • One and Two-tailed hypothesis tests
  • If you can predict a directional effect (if your treatment will cause the sample to do better, rather than worse), this is a one-tailed test. The relevant critical region lies in either the upper or lower tail of the distribution, not both.
  • If you cannot predict a direction of the effect ahead of data collection, you can use a general hypothesis (the treatment will have an effect, but don’t specify better/worse). This is a non-directional, two-tailed test. The critical regions that we will need to check lie in both the upper and lower ends/tails of the distribution.

Chapter 5– Hypothesis Testing with Means of Samples

  • What is the “distribution of means”?
  • What are its characteristics (its mean (M), variance (2M), and standard deviation (M))? You’ll be given the formulas…but know that the mean of the distribution of means is equal to the mean of the population.
  • 1-sample z test:
  • Used to compare a sample mean to a known population mean, when population standard deviation is also known.
  • Null hypothesis is that there is no difference between the sample and population/comparison means.
  • z obtained = (M - M) / M
  • Find critical z score using Appendix A (normal curve table)
  • Decide whether to reject null or fail to reject null
  • What is the difference between point estimates and interval estimates?
  • What does a 95% confidence interval mean? A 99% confidence interval?
  • How can we do hypothesis testing by using confidence intervals? (that is, be familiar with the rule that after you construct your CI, check to see where the population comparison mean falls. If it’s within the CI  fail to reject the null; if it’s outside the CI  reject the null).
  • You should be able to both interpret a confidence interval and be able to do a hypothesis test using one (see point above).
  • Suggested practice problems are below (for all chapters):

Suggested calculation practice problems –

Ch1 - #3, 7

Ch 2 - #1, 7

Ch 3 - # 4, 10a

Ch 4 - # 3, 8a

Ch 5 - #2, 4, 7