To determine if the independent variable has a systematic effect on the dependent variable
Sampling error
The difference between the population and the sample that represents the population
Differences among groups (or conditions): Does the mean for one group differ from the mean of the other group?
Null hypothesis
Group 1 Mean = Group 2 Mean (M1 = M2), The IV did not have an effect on the DV
Alternative Hypothesis
M1 ≠ M2, M1 > M2, M1 < M2, The IV did have an effect on the DV
To determine differences between means, use the difference between the group means
To determine the variability around the group means, use the amount of variability within each condition
Ratio of variability between groups to variability within groups
The more extreme the ratio, the more likely it is that group differences were the result of your manipulation and not sampling error
Calculating a t-value and p-value
1. t = (M1 - M2) / SE_difference
2. Use the t-value to calculate a p-value
3. Compare the p-value to the alpha level
Null Hypothesis Test
If p > .05, fail to reject the null-hypothesis, no good evidence that our groups differ, we cannot say that our IV had an effect
If p < .05, reject the null-hypothesis in favor of the alternative, there is good evidence that our groups are not equal, we can say that our IV had an effect
For t to be equal to zero, the mean difference needs to be equal to the mean difference we would expect due to sampling error
If the mean difference is equal to the mean difference we would expect due to sampling error, there is no effect, our differences can be explained by sampling error
Sufficient information to allow readers to understand the test conducted: test statistic, degrees of freedom, p-value, one-tailed or two-tailed test, Ms, SDs, Ns, effect sizes with confidence intervals
Writing results in APA format
Without effect size: Words words words, t(127) = 3.51, p = .031, one-tailed
With effect size: Words words words, t(127) = 4.52, p < .001, two-tailed, d = 0.75, 95% CI [0.50, 1.00]