How To Get the P-Value in Excel2 Easy Ways!
The p-value is useful for regression analysis and correlation in Excel.
It can be helpful in determining if a result is practical as well as helping to identify the best data set from the results to work with.
The value of a p-value can be anywhere from 0 to 1.
You can use Excel to find the p-value of a data set, but since Excel does not have a built-in method for finding the p-value, you will need to use other functions.
Explaining P-Value
P-value means probability value, and it tells you the likelihood of your results have occurred if the null hypothesis were true.
This value is typically written as a percentage when it is used in hypothesis testing to either reject or support a null hypothesis.
Decimal points can be used to show the p-value, but percentages are a better way of showing p-values as it makes the information being displayed clearer.
When a test is run to obtain a p-value, a low p-value shows that there is strong evidence against the null hypothesis and the alternative hypothesis is more likely to be true.
Alternatively, a high p-value indicates the evidence against the null hypothesis is weak.
The P-value in Excel
It can be quite difficult to find the p-value manually. So, we are going to explain two different ways of finding the p-value in Excel.
The first way we will explain this is by using the built-in Excel T-Test function. Then, we will explain how to use Excel’s Data Analysis Toolpak.
Calculating the P-Value by Using Excel’s Built-In T-Test Function
The T-Test is a simple way to find the p-value. It will allow you to conclude whether or not the null hypothesis is true.
In this example, we will use Excel’s built-in T-Test function. This function will provide the p-value result immediately.
To use this function, the first step you will need to take is to find the difference in run times before and after completing the program.
Once you enter the formula =B2-C2 in D2, use the fill handle to copy the formula to the remaining cells. Then, choose a cell in your spreadsheet and open the T-Test by typing =T.Test.
Next, choose array one as the before program column. Then select array two as the after-program column.
This will be a one-tailed distribution, and the type you will choose is paired. Once you do this, you can close the formula, and the p-value will be given.
The p-value for this test is 0.001, which is smaller than the alpha value and means there is strong evidence against the null hypothesis, and the alternative hypothesis is more likely to be true.
Here are some essential points to keep in mind when using the T-Test in Excel.
- The significance level (alpha value) can be changed at different levels allowing you to use Excel to obtain p-values at varying points.
- 05 and 0.01 are the most common significance levels.
- A p-value of less than 0.05 indicates that the data is very significant. Whereas a p-value that is less than or equal to 0.05 would mean the data is significant.
Calculating the P-Value by Using Excel’s Data Analysis ToolPak
Excel’s Data Analysis ToolPak is a good choice for calculating p-values for individuals who want more information about their data distributions.
The Data Analysis ToolPak is an add-in. So, if you decide you want to use the Data Analysis Toolpak, you will need to upload it into your copy of Excel.
Here are the steps you need to take to do this.
- Go to the File tab and click on it.
- Now, choose Options when you see the menu.
- You should now see “an “Excel Options” window. From the sidebar on the left, select “Add-ins.”
- When you see the drop-down box beside “Manage,” be sure that the “Excel Add-ins” option has been selected.
- Next, click on go, and you should see the “Add-Ins” dialog box. Check to ensure the “Analysis ToolPak” box has a checkmark.
- Then, click on Ok, and you should find the Data Analysis ToolPak as an option in Analysis under the Data tab.
Next, we will explain how to find the p-value using the Data Analysis ToolPak.
- Locate the Data tab and choose “Data Analysis.”
- Then, choose the “t-Test: Paired Two Sample for Means” under “Analysis tools.”
- When the next window opens, you will be able to select the inputs that are required to perform the t-test.
Here are the inputs you’ll need to enter.
- Variable 1 Range: Type the range of cells that the first data set are located in.
- Variable 2 Range: Type the range of cells that the second data set is located in.
- Hypothesized Mean Difference: You can put in the hypothesized mean here if you want. It is optional.
- Labels: You will place a checkmark in this box if you include labels in the ranges for variable one or variable two.
- Alpha: You have the option of entering your required alpha value or just leaving the .05 default value.
- Output Options: This is where you choose where your results will be displayed.
Here is an example using this method.
Suppose you are given data on a training program to increase running speed.
Here is the data for you to use to test the null hypothesis.
As with the method above, the first step you will need to take is to find the difference in run times before and after completing the program.
Once you enter the formula =B2-C2 in D2, use the fill handle to copy the formula to the remaining cells.
Next, find the “Data” tab and click on “Data Analysis.” Then, choose “T-Test: Paired Two Sample for Means” in the dialog box.
This should bring up another dialog box in which you should choose “Variable 1 Range,” the before program column.
Then choose “Variable 2 Range,” the after-program column.
The alpha value we will use is 5%, which is the default value. .01 and .05 are typical values to use as significance levels.
Next, choose your “Output Range.”
This is where the results will be shown.
Now you can click OK. This will give the results.
The p-value for the one-tail test is 0.001, and the p-value for the two-tail test is 0.002.
Therefore, since the p-value is smaller than the alpha value, there is strong evidence against the null hypothesis.
Final Thoughts
We showed you two ways to obtain the p-value in Excel.
The first method is easier and sufficient in most cases.
However, the second method is the better choice if you want a more thorough analysis of your data.