What are the three important characteristics of the sampling distribution of the mean (SDM)?

What factors influence the size of the SEM? Explain how these factors impact the size of the SEM.

What advantage does a larger sample have compared to a smaller sample? 

What does the central limit theorem tell us about the shape of the SDM? 

What are the steps involved in testing a hypothesis?

Why do we want to reject H₀?  Can we ever prove that a hypothesis is true?

How does the level of significance (alpha) influence the critical value(s)?

What does it mean if a result is statistically significant?

What are the four possible outcomes of a hypothesis test?

How can we reduce the probability of a type I error?

How can we increase the power of a statistical test?

What are the assumptions of using the z-test?  When can we use the z-test?

When should we use the t-test instead of the z-test?

In what ways do the sampling distributions of t and z differ?

What do degrees of freedom have to do with the sampling distribution of t?

Why to we used pooled variance to calculate the estimated standard error for independent samples t-tests?

How is t influenced by the variability of the samples and the difference between sample means?

When would you use an independent samples design? A repeated measures design?  A matched participants design?

What are the advantages and disadvantages of each type of design?

How do individual differences influence the value of the t?

What are the assumptions of the z-test, single sample t-test, independent samples t-test, and related samples t-tests?

What is homogeneity of variance and why is it important?

Determine probabilities for binomial variables using the normal approximation.

Calculate the mean and standard deviation of the SDM (i.e., the expected value of the mean and the SEM).

Compute a z-score that specifies the location of a particular sample mean within the SDM.

Determine the probability of obtaining a specified sample mean from a given population.

Distinguish single sample, independent samples, and related samples (repeated measures & matched participants) designs.

Identify independent variables, the levels of an independent variable, and dependent variables.

Identify what type of test statistic should be used to test a particular research hypothesis.

Translate a research question into statistical hypotheses (H₀ and H₁).

Find the rejection region and critical value(s) for a given alpha level and use this information to formulate a decision rule.

Compute the z- and t-test statistics.

Determine whether or not to reject H₀.

Interpret the results of a statistical test.

Interpret SPSS output for t-tests.

Use Hartley’s F-max test to assess homogeneity of variance.

binomial data

binomial distribution

normal approximation

central limit theorem

sampling distribution of the mean (SDM)

expected value of the mean

standard error of the mean (SEM)

hypothesis

null hypothesis (H₀)

alternative hypothesis (H₁)

critical value

critical (rejection) region

decision rule

non-directional (two-tailed) hypothesis test

directional (one-tailed) hypothesis test

z test statistic

level of significance

type I and type II errors

p value

alpha (a) = p(type I error)

beta (b) = p(type II error)

power (1-b) = p(rejecting false H0)

statistically significant vs. statistically reliable

t-statistic

t distribution

degrees of freedom

estimated standard error

independent v. related samples t-tests

independent samples design

repeated measures design

matched participants design

experiment v. quasi-experiment

independent and dependent variables

homogeneity of variance

pooled variance

Hartley’s F-max test

difference score

mean difference

individual differences

carry-over effects

progressive error

counterbalancing