Basic Statistics for Clinicians_1. Hypothesis Testing.pdf

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[ statistics * statistique ] BASIC STATISTICS FOR CLINICIANS: 1. HYPOTHESIS TESTING Gordon Guyatt, *t MD; Roman Jaeschke, *t MD; Nancy Heddle, t MSc; Deborah Cook, *t MD; Harry Shannon, * PhD; Stephen Walter, * PhD In the first of a series of foLur articles the authors explaiii the statistical concepts of hypothesis testing and p values. In many clinical trials investigators test a null hypothesis that there is no difference between a new treatmenit and a placebo or between two treatments. The result of a single experiment will almost always show some difference between the experi- mental and the control groups. Is the difference due to chatnce, or is it large enough to reject the null hypothesis atnd conclude that there is a true difference in treatment effects.= Statistical tests yield a p value: the probability that the exper- iment woulld show a difference as great or greater than that observed if the null hypothesis were true. By conivention, p valies of less than 0.05 are considered statistically significant, and investigators conclude that there is a real difference. However, the smaller the sample size, the greater the chance of erroneously concluding that the experimental treatment does not differ from the control - in statistical terms, the power of the test may be inadequate. Tests of several out- comes from one set of data may lead to an erroneouLs conclu- sion that an outconie is significant if the joint probability of the oLutcomes is not taken inlto accounit. Hypothesis testing has limitations, which will be discussed in the next article in the series. C linicians are often told that they are supposed to not only read journal articles, but also understand them and make a critical assessment of their validity. 2 Clinicians may offer better care if they are able to appraise critically the original literature and apply the results to their prac- tice.34 Criteria for assessing the strength of the methods reported in medical articles can provide clinicians with guidance in recognizing the strengths and weaknesses of Dans ce premier article duLlne serie de quLatre, les auteurs ex- pliquent les concepts statistiques que sont les vilrifications des hypotheses et les valeurs p. Au cours de niombreux essais cliniqLues, les enqueteurs font l'essai d/une hypoth&se nulle selon laquelle ii n'y a pas de difference entre uin nouveau traitement et uin placebo, ou entre deux traitements. Le r6suLl- tat d une seule experience indiquera presque toujouirs on ecart entre les sujets de l1exp&rience et ceux des groupes t6moins. L'6cart est-il attribLiable au hasard ou est-il assez important pour quon rejette Vlhypoth&se nulle et conclue qu'il y a vrai- ment uin 6cart entre les effets des traitenments? Les tests statis- tiques donnent une valeur p cest la probabilit6 selon laquelle l1exp6rience d6montrera un ecart aussi important ou plus im- portant que celLi qu'on observerait si lhypothese nulle s'av6- rait. Par convention, les valeors p de moins de 0,05 sont con- siderees conime importantes sur le plan statistique et les

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