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STATS module 2
STATS midterm 2.0
| Term | Definition |
|---|---|
| What is chance? | baseline explanation we assume for results |
| What does a null hypothesis hypothesise? | the average score in the two populations are exactly equal |
| What are Independent Samples? | membership in one group precludes membership in the other group |
| What is the generic t-test formula? | t = (sample data - hypothesized population parameter)/ estimated standard error |
| What do one sample t-tests test (general)? | if the sample data deviate from a hypothesized value in the population |
| What do larger t values indicate? | greater likelihood of discrepancy from the hypothesized value |
| What do independent samples t-tests assess? | the hypothesis that any difference in sample means is attributable to chance |
| What does a large t-value say about the likelihood that our two samples were drawn from two populations that do not differ in their mean scores? | The larger the t value, the less likely it is that our two samples were drawn from two populations that do not differ in their mean scores. |
| What does a null hypothesis do to our independent samples t-test formula? | (𝜇1 − 𝜇2 = 0) so we can drop it from the formula |
| What does S reflect? | the dispersion of scores in a sample around the sample mean |
| What is the relationship between the error in our sample mean, dispersion, and sample size? | the error in our sample mean decreases as dispersion decreases and sample size increases |
| How many sources of error do we have in an independent samples t-test and why? | two because there are two sample means |
| What does the independent sample t-test standard error equation do differently from the general standard error formula? | pools the standard errors of the two means |
| What is one downfall of pooling error? | things can get more complicated if the samples vary in size |
| What is the formula for calculating degrees of freedom for independent samples t-tests? | 𝑑𝑓 = (𝑛1−1) + (𝑛2 − 1) |
| What needs to be true in order to determine how likely any t value is? | we need to know the degrees of freedom AND the means need to be equal |
| What is the mean of a t-value distribution? | 0 |
| What do few degrees of freedom say about the t-value distribution? | the values tend to spread out from 0 |
| As degrees of freedom increase, what happens to the t-value distribution? | values cluster closer to 0 so that the shape of the curve approaches a normal distribution |
| What increases degrees of freedom? | sample size |
| Alpha is the chance of _______________ that we are willing to accept. | Type I error |
| WHAT IS TYPE I ERROR? | concluding a mean difference exists in the populations (rejecting the null) when there is actually no difference (the null is true) |
| A significant one-tailed t value p = .05 will correspond to a two-tailed t value with a p = ____. | .10 |
| What is a lopsided test? | differentially weight the tails of the distribution |
| A test has a more liberal threshold for the predicted direction and a more conservative threshold for the unexpected direction. What kind of test is this? | lopsided test |
| WHAT IS TYPE II ERROR? | concluding there is no mean difference between our populations (accepting or failing to reject the null) when there is actually a difference in means between the populations (the null is false) |
| What is power? | the probability that a statistical test will correctly reject a false null hypothesis |
| What is the relationship between Type II error and power | inverse relationship: power = 1 - β |
| What are the three determinants of power? | alpha (and one vs two tailed), sample size, effect size |
| What are the two types of power tests? | a priori and post hoc |
| What is an a priori power test used for? | determining an appropriate sample size for a specified alpha and power, and making an effect size assumption |
| Is power an issue of false negatives or false positives? | both |
| What are two reasons power may be low? | small effect size, small sample size |
| What assumptions regarding the data is the independent samples t-test based on? | 1. independence of observations 2. The distribution of the outcome variable should be normally distributed in each group 3. Homogeneity (equality) of variance in the outcome variable across the groups |
| What is the purpose of a repeated measures t-test | Testing a difference between two means for the same sample of people |
| What does the sample data for repeated measures t-tests look like? | difference scores (e.g.: difference in Time 1 and Time 2 scores, difference in condition 1 score and condition 2 score) |
| What does the letter D represent in equations? | sample data difference scores |
| How is the mean of difference scores represented? | D-bar |
| What is the formula for the degrees of freedom in a repeated measure's t-test and why? | df = n- 1, there is only 1 sample |
| What assumptions regarding the data is the repeated measures t-test based on? | 1. independence of observations 2. difference scores are normally distributed |
| What factors affect the size of a t-value in a repeated measures t-test? | 1. magnitude of difference 2. standard deviation of difference scores 3. sample size |
| What factors affect the size of a t-value in an independent samples t-test? | 1. magnitude of difference 2. dispersion in samples 3. sample size |
| What are some advantages/ disadvantages of independent samples vs. repeated measures designs? | 1. Repeated measures have more power 2. Repeated measures are more economical 3. Independent samples have no carry over effects 4. Independent samples less vulnerable to demand characteristics |
| What is the most common influencer of choosing between independent samples vs. repeated measures designs? | the nature of the question or variable determines the design |
Created by:
c.trepanier
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