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The appropriate test to use for this scenario is the **Goodness of Fit** test. Here's why:

The Goodness of Fit test is used when you want to determine if the observed distribution of a single categorical variable (in this case, the color distribution of M&Ms) matches a given theoretical distribution (the percentages claimed by the company). 

In this problem:
- The company provides the expected percentages of each color.
- The student collects a sample and observes the actual counts for each color.

By performing a Goodness of Fit test, the student can determine whether the observed counts significantly deviate from what is expected based on the company's claimed distribution.

So, the correct choice is: **Goodness of Fit**.

### Would you like details or have any questions about this?

Here are 5 related questions:
1. How do you calculate the expected frequency for each category in a Goodness of Fit test?
2. What is the formula for the chi-square statistic in the Goodness of Fit test?
3. When should you use a Test for Independence instead of a Goodness of Fit test?
4. What are the degrees of freedom in a chi-square Goodness of Fit test?
5. How do you interpret the p-value from the Goodness of Fit test?

**Tip:** Make sure the sample size is large enough so that all expected frequencies are at least 5 to satisfy the conditions for using the chi-square distribution effectively.

A student wanted to know if M&Ms follow their stated distribution. The company claims certain percentages for each color (Brown: 15%, Yellow: 12%, etc.). The student takes a random sample of 600 M&Ms and observes the actual counts for each color. What type of test should be used to test that M&M follows their stated distribution?

Determine if a sample of M&Ms follows the company's stated color distribution using the Chi-square Goodness of Fit test. This problem involves calculating expected values and comparing them to observed values for various colors, including Brown, Yellow, and Blue. Suitable for students in Grades 10-12.

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