Statistical significance is an important concept in psychological research. Referencing both the null hypothesis and the research hypothesis, explain the logic of statistical significance testing. What do researchers infer when they find a statistically significant result? Include in your discussion an explanation of both Type I error and Type II error.

Question

What will be an ideal response?

Answer 1

Ans: Varies.

Statistical significance helps researchers evaluate the likelihood that their results are due to chance. Researchers may have an interest in learning about the research hypothesis, which is their prediction of a particular difference or relationship, but they test the null hypothesis. Researchers use probability to determine the likelihood that their results would occur if the null hypothesis were true. If there is low likelihood, researchers reject (never disprove!) the null hypothesis. Note that researchers never prove the research hypothesis right or wrong; they simply use probability to evaluate their own findings against the null hypothesis. Type I error occurs when the null hypothesis is incorrectly rejected. The probability of this occurring is the same as the p-value established by the researcher. Type II error occurs when the null hypothesis is incorrectly accepted. This is an unfortunate case for a researcher whose predicted relationship is actually true.