I need help with this please.
A researcher is designing a research study. He hopes to show that the results of an experiment are statistically significant. What size of p value would take?
a) Large value of p
b) Small value of p
c) The size of the p value does not affect the statistical significance
I believe the correct answer is b. What do you believe?
P = probability that rejecting the null hypothesis is due solely to chance. I would agree with b.
Thank you very much PsyDAG.
can you help me with the other one i posted related to this please? (multiple choice)
I agree with you, the correct answer is b) a small value of p.
To understand why, let's break it down:
The p-value is a measure of statistical significance, often used in hypothesis testing. It represents the probability of obtaining a test statistic as extreme as the one observed (or even more extreme), assuming that the null hypothesis is true.
In general, a smaller p-value indicates stronger evidence against the null hypothesis. If the p-value is very small (typically less than a predetermined threshold, such as 0.05), it suggests that the observed results are unlikely to occur by chance alone if the null hypothesis is true. This is often interpreted as evidence to reject the null hypothesis and conclude that the alternative hypothesis is likely.
On the other hand, if the p-value is large, it suggests that the observed results are likely to occur by chance alone, even if the null hypothesis is true. In such cases, we fail to reject the null hypothesis and therefore do not have strong evidence in favor of the alternative hypothesis.
So, in the context of the researcher hoping to show statistical significance, they would want a small p-value because it provides stronger evidence against the null hypothesis and supports the alternative hypothesis.