In the last exercise, we ran three separate 2-sample t-tests to investigate an association between a quantitative variable (amount spent per sale) and a non-binary categorical variable (location of VeryAnts visited, with options A, B, and C). The problem with this approach is that it inflates our probability of a type I error; the more tests we run, the worse the problem becomes!

In this situation, one approach is to instead use *ANOVA* (Analysis of Variance). ANOVA tests the null hypothesis that all groups have the same population mean (eg., the true average price of a sale is the same at every location of VeryAnts).

In Python, we can use the SciPy function `f_oneway()`

to perform an ANOVA. `f_oneway()`

has two outputs: the F-statistic (not covered in this course) and the p-value. If we were comparing scores on a video-game for math majors, writing majors, and psychology majors, we could run an ANOVA test with this line:

from scipy.stats import f_oneway fstat, pval = f_oneway(scores_mathematicians, scores_writers, scores_psychologists)

If the p-value is below our significance threshold, we can conclude that at least one pair of our groups earned significantly different scores on average; however, we won’t know which pair until we investigate further!

### Instructions

**1.**

The same data from the previous exercise is available to you in the workspace: costs of sales made at three locations of VeryAnts (saved as `a`

, `b`

, and `c`

).

Perform an ANOVA test on `a`

, `b`

, and `c`

and store the p-value in a variable called `pval`

, then print it out.

**2.**

At a .05 significance level, does this p-value lead you to reject the null hypothesis (and conclude that at least one pair of stores have significantly different average sales)?

Change the value of `significant`

to `True`

if the p-value indicates at least one pair of stores have significantly different sales and `False`

otherwise.