the hypothesis has not been proven wrong.
It means that the experiment is consistent with the hypothesis. It adds to the credibility of the hypothesis.
If a scientist fails to reject a hypothesis, it means that the evidence gathered from their experiments or observations was not strong enough to disprove the hypothesis. This does not confirm the hypothesis as true; instead, it suggests that there is insufficient evidence to support an alternative explanation. It is important to note that failing to reject a hypothesis does not provide proof of its validity, and further research may be needed to draw more definitive conclusions.
It means there is no reason why he should reject it, whether because there is no evidence to the contrary or because an experiment set up to test it affirmed that hypothesis.
If a scientist fails to reject a hypothesis, it means that the data collected from experiments or observations did not provide sufficient evidence to disprove that hypothesis. This does not necessarily prove the hypothesis to be true; rather, it indicates that there is not enough support to conclude it is false. The results may suggest that further research is needed to explore the hypothesis more thoroughly. Ultimately, the failure to reject a hypothesis is a part of the scientific process and contributes to the ongoing evaluation of scientific theories.
the hypothesis has not been proven wrong.
Depending on the results of that test, either accept or reject that hypothesis.
It means there is no reason why he should reject it, whether because there is no evidence to the contrary or because an experiment set up to test it affirmed that hypothesis.
It means that she or he has to accept that the existing hypothesis appears to be true.
It means there is no reason why he should reject it, whether because there is no evidence to the contrary or because an experiment set up to test it affirmed that hypothesis.
because he didn't know how the tectonic plates/continents moved
I no
If a researcher fails to reject the null hypothesis when it is actually false, they have made a Type II error. This occurs when the researcher incorrectly concludes that there is not enough evidence to support an alternative hypothesis, despite it being true. In contrast, a Type I error happens when the null hypothesis is rejected when it is actually true.