19 terms

# PSYC325 G.G.

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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