In the last exercise, we inspected a sample of 50 purchase prices at BuyPie and saw that the average was 980 Rupees. Suppose that we want to run a one-sample t-test with the following null and alternative hypotheses:

- Null: The average cost of a BuyPie order is 1000 Rupees
- Alternative: The average cost of a BuyPie order is
**not**1000 Rupees.

SciPy has a function called `ttest_1samp()`

, which performs a one-sample t-test for you. `ttest_1samp()`

requires two inputs, a sample distribution (eg. the list of the 50 observed purchase prices) and a mean to test against (eg. `1000`

):

tstat, pval = ttest_1samp(sample_distribution, expected_mean)

The function uses your sample distribution to determine the sample size and estimate the amount of variation in the population — which are used to estimate the null distribution. It returns two outputs: the t-statistic (which we won’t cover in this course), and the p-value.

### Instructions

**1.**

Use `ttest_1samp()`

to run the hypothesis test described above (null: the average price is 1000 Rupees; alternative: the average price is **not** 1000 Rupees).

Store the p-value in a variable called `pval`

. Remember that it is the second output of the `ttest_1samp()`

function. We won’t use the first output, the t-statistic, so you can store it in a variable with whatever name you’d like.

**2.**

Print out `pval`

to the console.

Does the p-value you got make sense, knowing the mean of `prices`

and having inspected the data? (Look at the hint for an answer to this question).