In statistical thinking there is a tenancy towards conservatism. The investigato

Question

In statistical thinking there is a tenancy towards conservatism. The investigators, enthusiastic to obtain positive results, may prefer favorable conclusions and may tend to over-interpret the data. It is the statistician's role to add objectivity to the interpretation of the data and to advocate caution.

On the other hand, the investigators may say that conservatism and science are incompatible. If one is too cautious, if one is always protecting oneself against the worst-case scenario, then one will not be able to make bold new discoveries.

Which of the two approach do you prefer?

When you formulate your answer to this question it may be useful to recall cases in your past in which you where required to analyze data or you were exposed to other people's analysis. Could the analysis benefit or be harmed by either of the approaches?

For example, many scientific journal will tend to reject a research paper unless the main discoveries are statistically significant (p-value < 5%). Should one not publish also results that show a significance level of 10%?

Explanation / Answer

It is definitely a statistician's role to communiate the results in a true manner, which should not be over-optimistic neither it should be very conservative. The significance level, though its value is taken as 0.05 in general, ideally depends on the problem statement. For some problems, the significance level of 10% also makes sense. For some problems, it will make more sense to be overly cautiuous and have significance level of 1%. But in the end, while communicating the results to the targeted audience, one should state clearly the interpretation of the significane level and confidence intervals, though it gets a little difficult to explain sometimes. So that the end user knows what are the precentage of cases in general where a model can go wrong and provide incorrect predictions.

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