How can we help?
You can also find more resources in our
Select a category
Something is confusing
Something is broken
I have a suggestion
What is your email?
What is 1 + 3?
Is my IV manipulating what I want it to manipulate?
Is my DV measuring what I want it to measure?
Is my experiment a fair test of my hypothesis?
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
H0 = There is no effect of the IV on the DV
e.g. Music has no effect on math
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)
The likelihood that you obtain the observed result (or a result more extreme), given the null hypothesis is true
0.25 at each end of the distribution curve
0.5 at the predicted direction of the distribution curve
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.
The bigger the difference between means, the bigger the effect size. The bigger the SD is, the smaller the effect size
Advantages: fewer subjects, more statistical power
Disadvantages: longer experiments, counterbalancing, carryover effects
Stratified Random Sampling
More specific populations e.g. gender, culture, handedness
More than 2 groups to be compared.
F = -------------------
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).
X = # of groups - 1
Y = total # - # of groups
When you have 4 groups, and you only want to compare 2 of them, use an independent t-test