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Which P value can be used as a safeguard against bias?

1) No P value safeguards against bias
2) 0.05
3) 0.01
4) 0.001
5) 0.0001

Answer :

Final answer:

No P value can completely safeguard against bias, as it partly depends on study design and execution. A smaller P value such as 0.01 or 0.001 may be chosen to reduce false-positive results, especially in critical research areas. Robust design and reporting practices are also essential to minimize bias.

Explanation:

The question of which P value can be used as a safeguard against bias involves understanding the nature of statistical significance and the potential for bias in research studies. No P value can completely safeguard against bias since biases can stem from various aspects of study design and conduct, such as the choice of a threshold, data dredging, or post hoc exclusion of participants. However, choosing a more stringent P value (e.g., 0.01 or 0.001) can help reduce the likelihood of a false-positive result.

Typically, P \<= 0.05 is the standard cutoff for statistical significance, reflecting a 5% probability of a Type I error, but this threshold is somewhat arbitrary and depends on the level of risk researchers are willing to accept. In cases where there is a higher consequence of a false-positive result, such as in medical research that could affect patient health, a smaller P value such as 0.01 or 0.001 may be used to indicate a more conservative approach and to minimize the risk of accepting a false effect as true.

It is also important to pre-establish criteria for data handling, such as exclusion criteria for study participants, to avoid introducing bias during the analysis phase. Ultimately, while smaller P values can potentially reduce the risk of error, they do not eliminate the possibility of bias, which must be addressed through robust study design and transparent reporting practices.

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