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Suppose that you have a research study about risks factors for kidney disease in a specific area (in the area live few people). In recent years there was a high prevalence of this disease (unfortunately many deaths) and you have the supposition that environmental factors affect kidney disease. A survey with a sample size of 50 was taken but the information of the persons who died was not taken account in the research.

The conclusion of the study was that the environmental factors does not affect kidney disease. Why the environmental effect does not affect kidney disease? How can you explain this result to other people (non-statistician) who believe fervently that the data must reflect the relationship between kidney disease and environmental factors?

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  • $\begingroup$ I am confused by how you phrase this question. It begins "suppose that you have ... research" but ends "what did I do wrong." Evidently something changed in the telling. What are you trying to ask? $\endgroup$
    – whuber ♦
    Commented Sep 4, 2015 at 17:25
  • $\begingroup$ There is no evidence that the environmental factors affect disease or there is evidence that they do not affect the disease (those are two different things)? Sample of size 50 is considered to be pretty small in many areas, so is it sufficient in this case? $\endgroup$
    – Tim
    Commented Sep 4, 2015 at 19:55

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Depending on how you approached your research, there could be several causes. Is it possible that outliers distorted the result across the quite small sample? Is it possible that there was mis-measurement in the independent variables?

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I would say that the conclusion was incorrect. The point of a significance test is to test the null hypothesis. The null hypothesis is either rejected, or it is not rejected.

The description implied that he null hypothesis was not rejected - but that is not the same as saying that the null hypothesis was accepted. The study searched for evidence of environmental factors; the study failed to find that evidence. That doesn't mean that it is not the case, it just means that the study failed to find it.

If you search for a needle in a haystack, and you don't find a needle, it doesn't mean that there isn't a needle there, it just means you didn't find one.

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