Degrees of freedom are the number of independent variables that can be estimated in a statistical analysis and tell you how many items can be estimated. It is denoted by n, which is the sample size minus one. It is a combination of how much data you have and how many parameters you can estimate.
Degrees of freedom for t-Tests
T-tests are hypothesis tests for the mean and use the t-distribution to determine significance. The degrees of freedom define the shape of the distribution that your t-tests use to calculate the p-value. The p-value is the probability of your data occurring under the null hypothesis.
Importance of Degrees of Freedom
- Cutoff values: In inferential statistical tests, having an understanding of DOF can help to determine critical cutoff values.
- Rejection of false hypothesis: More often than not, higher DOF realte to larger sample size. A higher ssample size allows for more power to reject a false hypothesis and determine a significant result.
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