The probability of rejecting the null hypothesis when it is true, is called
(A) co-variation
(B) assumption
(C) power
(D) chance value
Correct Ans: (C)
Explanation:
In media research, understanding statistical power is crucial when interpreting results from hypothesis testing. Statistical power refers to the probability of correctly rejecting the null hypothesis when it is actually true. Although this sounds counterintuitive, it reflects the chance of making a Type I error, where you detect an effect that doesn’t exist.
Let’s break this down. The null hypothesis often claims there is no effect or no relationship. When researchers collect data and run tests—such as t-tests or ANOVA—they make decisions about whether to reject that null hypothesis. However, if the null hypothesis is actually true and the test wrongly rejects it, a false positive occurs. That probability is what we call the statistical power of the test.
In media studies, this concept becomes important. For example, if a researcher studies whether political advertisements influence voting behavior and finds a significant result, they need to consider the power of their test. If the power is too high or the sample size is too large, they may reject the null even if the effect is minimal or due to randomness.
Now, let’s clarify why the other options are incorrect:
(A) Co-variation refers to a relationship between variables but not to hypothesis testing errors.
(B) Assumption relates to the conditions necessary for tests (like normality), not the risk of false conclusions.
(D) Chance value is not a valid statistical term—it may confuse readers with p-value, which reflects the probability of observing the data if the null hypothesis is true.
Thus, (C) Power is the correct answer. It is an essential part of evaluating the trustworthiness of research findings.
In conclusion, researchers in mass communication must carefully manage statistical power. Doing so helps them reduce the risk of misleading conclusions and strengthens the validity of their work.
