![]() ![]() ![]() In this case, alpha = 0.05 (the norm for biostatistics). ![]() What the table tells us is the required value of t that will give a significant result at a pre-determined level of alpha. It’s used to determine whether a given t-test statistic is significant or not (we’ll be talking t-tests shortly, but for now take my word for it). You may recognize the type of table shown below: It’s important that we understand how degrees of freedom (and thus, sample size) affect the significance of p-value testing. Generally, degrees of freedom are determined by sample size, and with increasing sample size we have increasing degrees of freedom. This is the basic formula for determining the degrees of freedom for a given statistical test. Our degrees of freedom are sample size (n) minus the estimated parameters (p). The reason we have one degree that cannot move is because we have estimated one parameter – in this case, the mean. So we have three degrees of freedom (n – 1). This final number is not free to move, its identity is fixed as soon as the other three are determined. We know the numbers add up to 20, so no matter what the first three numbers are the fourth must make them add up to 20. Now if I tell you the first three numbers: Therefore, the sum must be 20 since there are four numbers and the mean is five. You should be able to calculate the sum via some nifty reverse engineering. Pop Quiz: What is the sum of our four numbers? Even if you think you know how they work, let’s start from scratch and go through the process.įirst an example* we have four numbers. P-values are inherently linked to degrees of freedom a lack of knowledge about degrees of freedom invariably leads to poor experimental design, mistaken statistical tests and awkward questions from peer reviewers or conference attendees. In the last post I talked about p-values and how we define significance in null hypothesis testing. ![]()
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