True or False
Alpha level becomes zero only when both samples reflect different populations.
The alpha level becomes zero when you have sampled the whole population.
FAlse
False.
The alpha level, also known as the significance level, is a predetermined threshold used in hypothesis testing. It represents the maximum probability of incorrectly rejecting the null hypothesis when it is actually true. Typically, the most common alpha level used in statistics is 0.05, which corresponds to a 5% chance of Type I error.
The alpha level does not become zero only when both samples reflect different populations. In hypothesis testing, the null hypothesis assumes that there is no significant difference between the populations being compared. Therefore, the goal is to gather evidence to either reject the null hypothesis in favor of the alternative hypothesis or fail to reject the null hypothesis.
When a hypothesis test is performed, the p-value is calculated. The p-value represents the probability of obtaining the observed data, or more extreme data, under the assumption that the null hypothesis is true. If the p-value is less than or equal to the alpha level, it is considered statistically significant, leading to the rejection of the null hypothesis.
In summary, the alpha level does not need both samples to reflect different populations to be non-zero.