The „P“ value indicates the probability that the observed result occurred by chance. The „P“ value must be interpreted in the context of the „alpha“ or „type 1“ error that will be decided before the start of the study. Type 1 error is the probability of finding by chance a difference between treatments if a difference does not exist. It is conventionally set at 5%, which means that if the study finds a difference between treatments, we can be 95% sure that it is a real difference and not a fortuitous discovery. For a type 1 error of 5%, the corresponding „P“ value must be <0.05 to be considered statistically significant. Plus the "P" common pitfalls in statistical analysis: "P" values, statistical significance and confidence intervals In summary, CIs offer a set of values for which we can be reasonably sure that the truth resides, the direction and size of the difference between two groups as well as statistical significance; In contrast, P-values quantify the probability that the study results are due to chance (strength of the evidence), but do not provide a direct measure of the size or direction of the effect. While CIs provide more information than P-values, these two are complementary, and both authors should report in their papers. The perfect chord is evident when Cohen`s cappa is equal to 1 and a value equal to zero indicates that the chord is no better than what would have been obtained by chance. Pitfalls in Statistical Analysis – A Reviewers` perspective Sakir Ahmed1, Aadhaar Dhooria2 1 Department of Clinical Immunology and Rheumatology, Kalinga Institute of Medical Sciences, KIIT University, Bhubaneswar, Odisha, India2 Department of Rheumatology, Santokba Durlabhji Memorial Hospital, Jaipur, Rajasthan, India Why is this important? Well, after clinicaltrials.gov, the number of trials using Bland-Altman as primary analysis appears, but further study of some of these studies shows that this methodology is often misused and sample size calculations are not properly taken into account.

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