Understanding Grows When “P Value” Is Replaced with the “Confidence Interval” in Medical Toxicology

Afshari, Reza (2013) Understanding Grows When “P Value” Is Replaced with the “Confidence Interval” in Medical Toxicology. Asia Pacific Journal of Medical Toxicology, 2 (3). p. 81.

APJMT_Volume 2_Issue 3_Pages 81-81.pdf

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While working on the articles submitted to Asia Pacific Journal of Medical Toxicology, I realised that statistical approach and notably the concept of �P value� is somehow poorly understood and has taken surrealistic credibility within the community of medial toxicologists. In this editorial, common flaws on the subject have been summarized. P value, which is ranged from 0 to 1, is the probability of observing the result found from a study at least as extreme as the one that was actually observed when the null hypothesis (H0) is not rejected (1). The famous arbitrary cut-off point of 0.05 (alpha level) for separating the probable from the improbable was suggested by Sir Ronald Fisher in 1926 and still stands to date (2). This level was selected to distinguish real differences from random variability, and to minimise the contribution of random variability (3). Despite its advantages, P value realm of importance has recently been shaken in clinical studies. A significant P value means that there is a sufficient evidence to reject the null hypothesis, when the statistical design has been formulated before conducting the study (4). The P value ability to determine the truth about measured effects; however, is usually quite limited (5), as P value is confounded by a number of factors. If the sample size increases or the standard deviation decreases, the P value becomes smaller for the same mean difference (6). Any difference in very large samples would eventually become significant unless zero difference exists. P value has been widely misinterpreted in medical toxicology, especially when rare outcomes (number of deaths in each group) in very large samples (number of poisonings) are studied. P value is just hypothesis testing which makes it less interesting in clinical settings (7). Interpretation of P value is rather an area of philosophical uncertainty (5). The following notions are incorrect - as are commonly stated - to assume that P value (a) demonstrates a null hypothesis is true or false (b) excludes false significance (a P value of 0.01 means that repeated experiments might even lead to a non-significant P value) (c) likely reflects a causal relationship. In addition, P value provides no insight into the clinical relevance. It cannot tell how large or small the observed effect was (1). P value does not provide usefulness compared to the magnitude of differences between two findings (6). No theoretical basis exists for 5 probability or less to be strong enough evidence to reject all null hypotheses (5). P value should not be treated as �absolute�. The actual difference between 0.049 and 0.051 is very limited.  When multiple independent hypotheses are tested in one study the risk that one of the significant results is false positive increases. Comparison of P values across repeated studies with similar design is also not correct. It should be kept in mind that a low P value is not equivalent to a high level of precision (6). Reviewers should be aware that P value can be manipulated. By changing the arbitrary cut-off points in continuous data, statistical analysis may provide more than one answer, implying �data interpretation is more an art than science� (8). Authors may also push a

Item Type: Article
Subjects: QV pharmacology
Divisions: Journals > Asia Pacific J Toxicology
Depositing User: apjmt apjmt
Date Deposited: 12 Oct 2017 16:59
Last Modified: 12 Oct 2017 16:59
URI: http://eprints.mums.ac.ir/id/eprint/8176

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