# How do you do chi-square in Excel?

## How do you do chi-square in Excel?

Table of Contents

Calculate the chi square p value Excel: Steps

- Step 1: Calculate your expected value.
- Step 2: Type your data into columns in Excel.
- Step 3: Click a blank cell anywhere on the worksheet and then click the “Insert Function” button on the toolbar.
- Step 4: Type “Chi” in the Search for a Function box and then click “Go.”

## How do you do a chi-square step by step?

Let us look at the step-by-step approach to calculate the chi-square value:

- Step 1: Subtract each expected frequency from the related observed frequency.
- Step 2: Square each value obtained in step 1, i.e. (O-E)2.
- Step 3: Divide all the values obtained in step 2 by the related expected frequencies i.e. (O-E)2/E.

**What does p-value tell you?**

What exactly is a p-value? The p-value, or probability value, tells you how likely it is that your data could have occurred under the null hypothesis. It does this by calculating the likelihood of your test statistic, which is the number calculated by a statistical test using your data.

### How do you set up a chi square test?

How to perform a Chi-square test

- Define your null and alternative hypotheses before collecting your data.
- Decide on the alpha value.
- Check the data for errors.
- Check the assumptions for the test.
- Perform the test and draw your conclusion.

### How do you insert a chi-square symbol in Word 2013?

document and press Alt+X to insert the chi. The “square” part is a superscript 2. You can type the number 2, select it, and press Ctrl+Shift+= to make it superscript.

**What is the difference between t test and chi-square?**

T-Test vs. Chi-Square. We use a t-test to compare the mean of two given samples but we use the chi-square test to compare categorical variables.

## Is p-value of 0.05 Significant?

P > 0.05 is the probability that the null hypothesis is true. 1 minus the P value is the probability that the alternative hypothesis is true. A statistically significant test result (P ≤ 0.05) means that the test hypothesis is false or should be rejected. A P value greater than 0.05 means that no effect was observed.