Lecture 12 - wrap up and review Flashcards
what is the purpose of a confidence interval in hypothesis testing?
the purpose of a confidence interval in hypothesis testing is to provide a range of values within which the true mean (or mean difference) is likely to lie with a certain level of confidence (e.g. 95%). It offer an alternative to traditional significance testing by showing the range within which we can be confident the true mean lies
How do you calculate a 95% confidence interval for a sample mean?
a 95% confidence interval for a sample mean is calculated using the formula:
- see photo
- where M is the sample mean, t critical is the critical t value, and Sm is the standard error of the mean
Provide an example of calculating a 95% confidence interval for a sample mean
- in 1970, the CO2 level was 325 ppm. In 2000, the average level from 25 air tests was 360 ppm, with a standard error of 11.
To calculate the 95% confidence interval: - see photo attached
- therefore, the 95% confidence interval is (337.30, 382,70)
What is Cohen’s d and how is it used to measure effect size?
Cohen’s d is a measure of effect size that expresses the difference between two means in terms of standard deviations. It is calculated using the formula:
- see photo
- where M1 and M2 are the means of the two groups, and s pooled is the pooled standard deviation.
- Cohen’s d indicates the strength of the association between a two-level independent variable (IV) and a continuous dependent variable (DV)
How are effect size, sample size, alpha and power related?
effect size, sample size, alpha, and power are mathematically related. If you know three of these, you can calculate the fourth. For example:
- given an expected effect size of 0.4 and a sample size of 20, you can calculate the power
- given an expected effect size of 0.2, you can calculate the sample size needed for 80% power
- given a sample size of 50, you can calculate the smallest effect size detectable with 90% power
What are the assumptions of the independent groups t-test?
The assumptions of the independent groups t-test are:
- normality of sampling distributions
- homogeneity of variance between conditions (s1≈ s2)
- independence of observations (separate people in each group)
What are the assumptions of the repeated measures t-test?
The assumptions of the repeated measures t-test are:
- normality of sampling distributions
- homogeneity of variance between conditions
- difference scores come from a justified pairing of raw scores (two scores per participant)
A major purpose of randomly assigning study participants to experimental groups is?
Roughly matching the groups, on average, on all characteristics
Variance is the:
mean squared deviation
a researcher develops a new self-report questionnaire to measure the strength of a person’s psychological connection to a social group. People who have belonged to that social group for a long time tend to get higher scores on the new questionnaire than newcomers to the group. This evidence:
suggests that the new social identity measure has some validity
Study A has a larger sample and measures a variable that has high variability in the population. Study B has a smaller sample and measures a variable that has low variability in the population. Which study would likely get a more accurate estimate of its target population mean? why?
Study A, because it has a larger sample
Imagine two studies, one with a small sample and one with a large sample. The large sample would:
possibly be less representative than the small sample, if the studies used different sampling methods
In a correlation analysis, the presence of outliers:
can increase or decrease…
when drawing pairs of random samples from the same population, the most common difference between sample means within a pair will tend to be:
unknown; it depends on both sample size and population variance
