That implies that you have N1 + N2 degrees of freedom, and that you spend 2 of them estimating the 2 means. This test uses samples of size N1 and N2 from these two populations respectively. In a two sample t-test setting, you need to estimate the difference (or, more generally, a contrast), between the means of two different populations. The remaining N-1 can be 'spent' on estimating the standard deviation. Taking the mean and standard deviation from a sample of size N from a single population, we start with N DF, and 'spend' 1 of them on estimating the mean, which is necessary for calculating the standard deviation. In this article, degrees of freedom are explained through these lenses through some common hypothesis tests, with some selected topics like saturation, fractional DF, and mixed effect models at the end. You earn it by taking independent sample units, and you spend it on estimating population parameters or on information required to get compute test statistics. I personally prefer to think of DF as a kind of statistical currency. You can interpret degrees of freedom, or DF as the number of (new) pieces of information that go into a statistic. R-bloggers - blog aggregator with statistics articles generally done with R software. Kaggle Self posts with throwaway accounts will be deleted by AutoModerator Memes and image macros are not acceptable forms of content. Just because it has a statistic in it doesn't make it statistics. Please try to keep submissions on topic and of high quality. They will be swiftly removed, so don't waste your time! Please kindly post those over at: r/homeworkhelp. This is not a subreddit for homework questions. All Posts Require One of the Following Tags in the Post Title! If you do not flag your post, automoderator will delete it: Tag