Sometimes, when we run a hypothesis test, we simply report a p-value or a confidence interval and give an interpretation (eg., the p-value was 0.05, which means that there is a 5% chance of observing two or fewer heads in 10 coin flips).

In other situations, we want to use our p-value to make a decision or answer a yes/no question. For example, suppose that we’re developing a new quiz question at Codecademy and want learners to have a 70% chance of getting the question right (higher would mean the question is too easy, lower would mean the question is too hard). We show our quiz question to a sample of 100 learners and 60 of them get it right. Is this significantly different from our target of 70%? If so, we want to remove the question and try to rewrite it.

In order to turn a p-value, which is a probability, into a yes or no answer, data scientists often use a pre-set significance threshold. The significance threshold can be any number between 0 and 1, but a common choice is 0.05. P-values that are less than this threshold are considered “significant”, while larger p-values are considered “not significant”.

### Instructions

**1.**

Suppose that we ran a hypothesis test and calculated a p-value of .062 (saved as `p_value1`

in **script.py**). For a significance level of 0.05, is this p-value significant? Change the value of `p_value1_significance`

in **script.py** to `'significant'`

or `'not significant'`

to indicate your answer.

**2.**

Now, suppose that we ran another hypothesis test and calculated a p-value of .013 (saved as `p_value2`

in **script.py**). For a significance level of 0.05, is this p-value significant? Change the value of `p_value2_significance`

in **script.py** to `'significant'`

or `'not significant'`

to indicate your answer.