Answer : The p-value of 0.0743 is greater than alpha at 0.05; so we fail to reject the null hypothesis and conclude that there is no significant difference in the number of unique users before and after a change in policy.
In this question, the manager wants to know if the number of users has changed.
So, the null and alternate hypotheses are:
Null Hypothesis: [tex] {H_{0}}: \mu = 131,520 [/tex]
Alternate Hypothesis : [tex] {H_{1}}: \mu \not\equiv 131,520 [/tex]
Type of test : Two-tailed test
The level of significance is 95%
We can calculate alpha (α) as follows:
[tex] \alpha = 1- Confidence Level [/tex]
[tex] \alpha = 1- 0.95 [/tex]
[tex] \alpha = 0.05 [/tex]
The p value = 0.0743.
We use the following rules to arrive at a conclusion when p-values and alpha is given:
If [tex] p-value < \alpha [/tex], reject the null hypothesis
If [tex] p-value \geq \alpha [/tex], we don't reject the null hypothesis.
Since the p-value is greater than alpha, we don't reject the null hypothesis.