What is Degree of Freedom in Statistics?
In inferential statistics, we estimate a parameter of a population by calculating a statistic of a sample. The number of independent pieces of information used to calculate the statistic is called the degrees of freedom. The degrees of freedom of a statistic depend on the sample size. When the sample size is small, there are only a few independent pieces of information, and therefore only a few degrees of freedom. When the sample size is large, there are many independent pieces of information, and therefore many degrees of freedom.
Although degrees of freedom are closely related to sample size, they’re not the same thing. There are always fewer degrees of freedom than the sample size.
When we estimate a parameter, we need to introduce restrictions in how values are related to each other. As a result, the pieces of information are not all independent. To put it another way, the values in the sample are not all free to vary.
How to Calculate Degrees of Freedom in Independent Samples t Test?
Degrees of Freedom in Independent Samples t Test calculator uses Degrees of Freedom = Size of Sample X+Size of Sample Y-2 to calculate the Degrees of Freedom, Degrees of Freedom in Independent Samples t Test formula is defined as the number of values in the final calculation of a statistic that are free to vary. It varies based on the specific statistical test or analysis being conducted in the independent sample t test of given data sample. Degrees of Freedom is denoted by DF symbol.
How to calculate Degrees of Freedom in Independent Samples t Test using this online calculator? To use this online calculator for Degrees of Freedom in Independent Samples t Test, enter Size of Sample X (N_{X}) & Size of Sample Y (N_{Y}) and hit the calculate button. Here is how the Degrees of Freedom in Independent Samples t Test calculation can be explained with given input values -> 20 = 6+4-2.