Question

Suppose a revolutionary hypothesis has been proposed by a famous Professor Brainstorm, and if the truth be known, it really did not conform to facts (population parameters). In the formal language of hypothesis testing, it is the null hypothesis that is true, after all. (The professor interpreted his results at the 5 percent level). His research was published and he won accolades for his scientific breakthrough. What does this story say about hypothesis testing and research? What is the likelihood of this happening? There are a number of safeguards in place so this should not happen in the scientific world. What are some of these?

Answer 1

Answer:

As indicated by this story, we can infer that there are odds of making incorrectly determinations in a speculation testing if the test isn't led dealing with the necessities or suspicions. There might be wrong conclusion drawn if one doesn't follow the assumptions properly.

There are sometimes cases when the wrong conclusions are drawn even if the experiment/research was conducted properly. These are called Type I and II errors in statistical terms.

A type I error occurs when we reject true null hypothesis and type II error occurs when we fail to reject false null hypothesis.

To avoid happening of these, there are certain assumptions to be followed:

1. The sample data must be obtained independently..
2. The example ought to be substantial with the end goal to make an exact estimation of populace parameter.

3.    The population must be very large compared to the sample size