Degrees of freedom is a mathematical equation used in mechanics, physics, chemistry and statistics. The statistical application of degrees of freedom is quite broad and students can expect to need to calculate degrees of freedom early on in statistics coursework. Accurately calculating the degrees of freedom you have in an equation is vital since the number of degrees lets you know how many values in the final calculation are allowed to vary. Since statistics attempts to be as precise as possible, the degrees of freedom calculation is done often and contributes to the validity of your outcome.

Step 1

Determine what type of statistical test you need to run. Both t-tests and chi-squared tests use degrees of freedom and have distinct degrees of freedom tables. T-tests are used when the population or sample has distinct (discreet) variables. Chi-squared tests are used when the population or sample has continuous variables. Both tests assume normal population or sample distribution.

Step 2

Identify how many independent variables you have in your population or sample. If you have a sample population of N random values then the equation has N degrees of freedom. If your data set required you to subtract the mean from each data point--as in a chi-squared test--then you will have N-1 degrees of freedom.

Step 3

Look up the critical values for your equation using a critical value table. Knowing the degrees of freedom for a population or sample does not give you much insight in of itself. Rather, the correct degrees of freedom and your chosen alpha together give you a critical value. This value allows you to determine the statistical significance of your results.