You know that a hypothesis test is used to determine the validity of a null hypothesis. Once again, the null hypothesis states that there is no actual difference between the two populations of data. But what result does a hypothesis test actually return, and how can you interpret it?

A hypothesis test returns a few numeric measures, most of which are out of the scope of this introductory lesson. Here we will focus on one: p-values. P-values help determine how confident you can be in validating the null hypothesis. In this context, a *p-value* is the probability that, assuming the null hypothesis is true, you would see at least such a difference in the sample means of your data.

Consider the experiment on history and chemistry majors and their interest in volleball from a previous exercise:

- Null Hypothesis:
`"History and chemistry students are interested in volleyball at the same rates"`

- Experiment Sample Means:
`34%`

of history majors and`39%`

of chemistry majors sign up for the volleyball class

A hypothesis test on the experiment data that returns a p-value of `0.04`

would indicate that, assuming the null hypothesis is true and there is no difference in preference for volleyball between all history and chemistry majors, you would see at least such a difference in sample mean (`39%`

- `34%`

= `5%`

) only `4%`

of the time due to sampling error.

Essentially, if you ran this same experiment `100`

times, you would expect to see as large a difference in the sample means only `4`

times given the assumption that there is no actual difference between the populations (i.e. they have the same mean).

Seems like a really small probability, right? Are you thinking about rejecting the null hypothesis you originally stated?

### Instructions

**1.**

You are big fan of apples, so you gather `10`

green and `10`

red apples to compare their weights. The green apples average `150`

grams in weight, and the red apples average `160`

grams in weight.

You run a hypothesis test to see if there is a significant difference in the weight of green and red apples. The test returns a p-value of `0.2`

. Which statement (`st_1`

, `st_2`

, `st_3`

, or `st_4`

) indicates how this p-value can be interpreted?

Update the value of `interpretation`

with the string `"st_1"`

, `"st_2"`

, `"st_3"`

, or `"st_4"`

depending on your answer.