Final answer:
The statement 'There are no circumstances in which the test of significant approach to hypothesis testing performs better than the interval estimation approach' is false. The test of significance and interval estimation are two different statistical techniques used in hypothesis testing.
Step-by-step explanation:
The statement 'There are no circumstances in which the test of significant approach to hypothesis testing performs better than the interval estimation approach' is False.
The test of significance and interval estimation are two different statistical techniques used in hypothesis testing. The test of significance helps determine if there is a significant relationship between variables by comparing sample statistics to a null hypothesis, while interval estimation provides a range of values in which the population parameter is likely to fall within a certain level of confidence.
There are cases where the test of significant approach may be more appropriate, such as when the research question is focused on determining if there is a significant effect or relationship, rather than estimating the value of the population parameter. On the other hand, interval estimation is more suitable when the focus is on estimating the value of the population parameter.