Exam 1 Flashcards
What effect does sample size have on your sampling distribution? In other words, how does sample size effect sampling error?
As your sample size gets bigger, your distribution gets narrower. You focus more on the actual effect and have less error in your results.
How do the amount of predictor variables affect type I error?
More predictor variables can lead to more redundancy and therefore more Type 1 error
What are the limitations to null hypothesis significance testing?
1) flawed logic. We want to know the probability of our data given the null hypothesis, but we actually get the probability of the null hypothesis given our data. In order to calculate what we’re hoping for, we would need to use Baysian statistics.
2) Correlations are rarely actually zero.
3) Impedes our ability to move forward as a field because we keep comparing to zero rather than to previous findings.
4) Turns a decision continuum into a dichotomous decision.
5) .05 is arbitrary
6) Problem with how we use significance testing–we often confuse statistical significance with practical significance.
What are advantages to null hypothesis significance testing?
1) good at penalizing design weakness
2) objective measurement makes it easy to interpret your results
3) objectivity makes it hard for people to discount results they don’t like
4) Widely accepted measure of success
What are alternative approaches to significance testing?
1) confidence intervals
2) effect sizes
3) meta-analysis
What does a confidence interval give us that a significance test does not?
1) more if a continuum than dichotomous, so you get more information than just yes/no
2) you see the variability in results. The confidence interval may not include zero so it may be significant, but if it’s very large then we might not be that confident in it.
3) confidence interval is focused on the effect size
What factors affect statistical power?
Effect size, alpha level, and sample size
