Congratulations! You now know how to implement a one-sample t-test in Python and verify the assumptions of the test. To recap, here are some of the things you learned:

- One-sample t-tests are used for comparing a sample mean to an expected population mean
- A one-sample t-test can be implemented in Python using the SciPy
`ttest_1samp()`

function - Assumptions of a one-sample t-test include:
- The sample was randomly drawn from the population of interest
- The observations in the sample are independent
- The sample size is large “enough” or the sample data is normally distributed

### Instructions

As a final exercise, some data has been loaded for you with purchase prices for consecutive days at BuyPie. You can access the first day using `daily_prices[0]`

, the second using `daily_prices[1]`

, etc.. To practice running a one-sample t-test and inspecting the resulting p-value, try the following:

Calculate and print out a p-value for day 1 where the null hypothesis is that the average purchase price was 1000 Rupees and the alternative hypothesis is that the average purchase price was

**not**1000 Rupees. Print out the p-value.Run the same hypothesis tests for days 1-10 (the fastest way to do this is with a for-loop!) and print out the resulting p-values. What’s the smallest p-value you observe for those 10 days?

Try changing the null hypothesis so that the expected population mean that you’re testing against is

**different**from 1000. Try any numbers that you want. How do your p-values change?

Solution code can be found in **solution.py**