PSYC325 G.G.
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19 terms
Terms | Definitions |
|---|---|
 Construct Validity | Is my IV manipulating what I want it to manipulate? Is my DV measuring what I want it to measure? |
| Internal Validity | Is my experiment a fair test of my hypothesis? |
| External Validity | Do my findings generalise to other populations, or other variables? |
| Reject Null when Null is true | Type 1 error |
| Accept Null when Null is not true | Type 2 error |
| Null Hypothesis | H0 = There is no effect of the IV on the DV e.g. Music has no effect on math |
| Research Hypothesis | H1 = There is an effect of the IV on the DV e.g. Music has an effect on math |
| t = 0 | No variability between groups (they are drawn from same population) |
| p-value | The likelihood that you obtain the observed result (or a result more extreme), given the null hypothesis is true |
| Two-tailed p-value | 0.25 at each end of the distribution curve |
| One-tailed p-value | 0.5 at the predicted direction of the distribution curve |
| Effect sizes | Measure of variability due to my effect divided by variability in my sample. P-value says nothing this. Different statistics have different measures of it. |
| Cohen's d | The bigger the difference between means, the bigger the effect size. The bigger the SD is, the smaller the effect size |
| Within-subjects design | Advantages: fewer subjects, more statistical power Disadvantages: longer experiments, counterbalancing, carryover effects |
| Stratified Random Sampling | More specific populations e.g. gender, culture, handedness |
| Oneway ANOVA | More than 2 groups to be compared. Between-group variance F = ------------------- Within-group variance |
| Post-hoc tests | Comparisons of means after finding a significant F. Used when I have no hypothesis about how the means might differ from each other (2-tailed). |
| df(x, y) | X = # of groups - 1 Y = total # - # of groups |
| Multiple Comparisons | When you have 4 groups, and you only want to compare 2 of them, use an independent t-test |
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