Degrees of Freedom


< Statistics and Probability Definitions

When it comes to statistical data, the term “degrees of freedom” (df) refers to the maximum number of values that can be independently varied in a given sample. In other words, it’s a measure of how much freedom you have when selecting values for your data sample.

But how do you calculate degrees of freedom? Keep reading to find out.

Calculating Degrees of Freedom

There are two methods for calculating df:

  • Using the formula df = N – 1, where N is the number of items in your data sample. So, if your sample contains 4 items, your df would be 3 (4 – 1 = 3).
  • Using the formula df = k – 1, where k is the number of parameters being estimated. For example, if you’re estimating the mean weight loss for a low-carb diet, k would be 2 (one for the mean and one for the population standard deviation). Therefore, df = 2 – 1 = 1.

It’s important to note that degrees of freedom are not always whole numbers. For example, if you’re using a Continuous Distribution like the Normal Distribution, your df will be infinity because there are an infinite number of values between any two given points.

Conclusion

In statistics, degrees of freedom refer to the maximum number of values that can be independently varied in a given sample. There are two formulas for calculating df: df = N – 1 and df = k – 1. It’s important to note that degrees of freedom are not always whole numbers; for example, if you’re using a Continuous Distribution like the Normal Distribution, your df will be infinity because there are an infinite number of values between any two given points. Now that you know how to calculate degrees of freedom, put this knowledge into practice and see what insights you can glean from your data.


One response to “Degrees of Freedom”