In this segment, you will be doing the same hypothesis, using an alternative procedure, i.e. the **p-value** approach. This is the most common way of finding the significance of test results in the industry. Technically, the p-value is the probability of observing values which are similar to or more extreme than the observed value given that the null hypothesis is true. This metric also enables us to determine whether the given test statistic gives us sufficient evidence to reject or fail to reject the null hypothesis. Let’s go ahead and see it in action in the next video

So, when you computed the p-value, you sort of followed an opposite route to the original critical value route. First, you computed the probability corresponding to the given Z-score = -1.756, which came out to be 0.0395. Since this is a two-tailed test, you multiplied this value with 2 and obtained the p-value as 0.079. Now, since this value is greater than the given level of significance, you fail to reject the null hypothesis.

To summarise, the p-value approach also gives us the same result as the critical value approach, which is that either both will reject the null hypothesis or fail to reject the null hypothesis.

In the upcoming segment, we’ll be making a slight twist to the null hypothesis and then observe how the given hypothesis statement changes.