Exercise

# Summarizing opportunity cost (2)

Now that you've created the randomization distribution, you'll use it to assess whether the observed difference in proportions is consistent with the null difference. You will measure this consistency (or lack thereof) with a p-value, or the *proportion of permuted differences less than or equal to the observed difference*.

The permuted dataset and the original observed statistic are available in your workspace as `opp_perm`

and `diff_orig`

respectively.

`visualize`

and `get_p_value`

using the built in `infer`

functions. Remember that the null statistics are above the original difference, so the p-value (which represents how often a null value is more *extreme*) is calculated by counting the number of null values which are `less`

than the original difference.

Instructions

**100 XP**

- First
`visualize`

the sampling distribution of the permuted statistics indicating the place where`obs_stat = diff_orig`

, and coloring in values below with the command`direction = "less"`

. - Then
`get_p_value`

is calculated as the proportion of permuted statistics which are`direction = "less"`

than`obs_stat = diff_orig`

. - As an alternative way to calculate the p-value, use
`summarize()`

and`mean()`

to find the proportion of times the permuted differences in`opp_perm`

(called`stat`

) are less than or equal to the observed difference (called`diff_orig`

). - You can test your knowledge by trying out:
`direction = "greater"`

,`direction = "two_sided"`

, and`direction = "less"`

before submitting your answer to both`visualize`

and`get_p_value`

.