Since the P-value (0.249) is greater than the significance level (α = 0.05), we fail to reject the null hypothesis. There is not enough evidence at the 5% significance level to conclude that the mean daily revenue has decreased after the price increase.
How can you know whether the information suggests that the mean daily revenue has decreased from its value before the price increase?
Null Hypothesis (H 0): The mean daily revenue has not decreased after the price increase, i.e., µ after ≥ µ before = $75.00.
Alternative Hypothesis (H a): The mean daily revenue has decreased after the price increase, i.e., µ after < µ before = $75.00.
Test Statistic:
We will use a one-sample t-test since we don't know the population standard deviation and have a small sample size (n < 30). The test statistic is calculated as:
t = (x - µ 0) / (s / √n)
t = (70.00 - 75.00) / (4.20 / √24) ≈ -1.19
Under the null hypothesis, the t-statistic follows a t-distribution with n-1 degrees of freedom (df = 23). We need to find the two-tailed P-value associated with t = -1.19 and df = 23.
Using a t-distribution table, we find the P-value ≈ 0.249. Alternatively, some statistical software tools can directly calculate the P-value.
Since the P-value (0.249) is greater than the significance level (α = 0.05), we fail to reject the null hypothesis. There is not enough evidence at the 5% significance level to conclude that the mean daily revenue has decreased after the price increase.