Degrees of freedom is a term used in statistics to indicate the number of independent values that can vary in an analysis without breaking any constraints. It is the number of independent pieces of information used to calculate a statistic. Degrees of freedom are commonly discussed in various forms of hypothesis testing in statistics, such as a chi-square. Degrees of freedom can describe business situations where management must make a decision that dictates the outcome of another variable.

In inferential statistics, you 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. The degrees of freedom of a statistic is the sample size minus the number of restrictions. Most of the time, the restrictions are parameters that are estimated as intermediate steps in calculating the statistic.

Degrees of freedom are related to sample size (n-1). If the degrees of freedom increase, it also stands that the sample size is increasing; the graph of the t-distribution will have skinnier tails, pushing the critical value towards the mean.

When determining the mean of a set of data, degrees of freedom are calculated as the number of items within a set minus one. This is because all items within that set can be randomly selected.

In summary, degrees of freedom is the number of independent values that can vary in an analysis without breaking any constraints. It is used in various forms of hypothesis testing in statistics, and it is calculated by subtracting the number of restrictions from the sample size. Degrees of freedom are related to sample size and are commonly used in determining the mean of a set of data.