Excel for Statistics > Chi Square P Value Excel
Watch the video on how to calculate a chi square p value Excel. Or, read the step-by-step article below.
P-values are used in hypothesis testing to help you figure out if your results are significant or not. A significant result is one where you reject the null hypothesis. In hypothesis testing, you’re really asking two questions:
- What do the results tell me about a population?
- What is the strength of those results?
A p-value is a number between 0 and 1, but it’s easier to think about them as percentages (i.e. a p-value of 0.05 is 5%). Small p-values (generally under 5%) usually lead you to reject the null hypothesis.
Calculate the chi square p value Excel: Steps
Step 1: Calculate your expected value. The expected value in chi-square is found by dividing your counts (the number of responses or data items) by the number of categories. There are twelve categories (zodiac signs) in the question, so:
29 + 24 + 22 + 19 + 21 + 18 + 19 + 20 + 23 + 18 + 20 + 23 = 256
256 / 12= 21.333
Step 2: Type your data into columns in Excel. For this sample question, type your zodiac signs into column B, the observed values in column C (the observed values are the counts in the question) and your expected value (from Step 1) in column D.
Step 4: Type “Chi” in the Search for a Function box and then click “Go.”
Step 5: Select “CHITEST” from the list and then click “OK.”
Step 6: Type a range into the “Actual Range” box for your observed values. For this sample problem, the observed values are in cells C3 to C14, so type “C3:C14.”
Step 7: Type a range into the “Expected Range” box for your expected values. For this sample problem, the observed values are in cells D3 to D14, so type “D3:D14.”
Step 8: Click “OK” to calculate the p-value in Excel, which for this sample problem is .9265.
That’s how to find a chi square p value Excel!
If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.Comments are now closed for this post. Need help or want to post a correction? Please post a comment on our Facebook page and I'll do my best to help!