What is effect in hypothesis testing?
An effect is a difference or disparity between what is thought to be true in a population and what is observed in a sample. In hypothesis testing, an effect is not significant when we retain the null hypothesis; an effect is significant when we reject the null hypothesis.
What is type II error in statistics?
A type II error is a statistical term used within the context of hypothesis testing that describes the error that occurs when one accepts a null hypothesis that is actually false. A type II error produces a false negative, also known as an error of omission.
When is a marginal effect statistically significant?
Marginal effects are typically (but not always) non-linear functions of all the estimated parameters and explanatory variables. So even if particular coefficient or OR is “statistically significant”, it doesn’t guarantee that the marginal effect associated with that coefficient is “statistically significant”.
How does non linearity affect the marginal effect?
Failure to take into account that the coefficients and odds ratios are different metrics from the marginal effect on probability. The non-linearity of these models then produces “paradoxical” results.
Why is the odds ratio significant but not the marginal effect?
The question is not really about why odds ratio is “significant” while the corresponding marginal effect is not. It is really about what does it mean when the respective p-values are very ‘far’ apart (e.g. the p-value for the odds ratio is .002 an that for the marginal effect is .5).
What does it mean when an effect is not statistically significant?
They think that statistically significant means “there is an effect” and not statistically significant means “there is no effect.” Which again leads them to freak out when they get these apparently contradictory results. For these people, what is needed is re-education about the concept of statistical significance.