Dept. of Community Medicine, Ahmadu Bello University, Zaria, Nigeria

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Dept. of Community Medicine, Ahmadu Bello University, Zaria, Nigeria P – VALUE, A TRUE TEST OF STATISTICAL SIGNIFICANCE? A CAUTIONARY NOTE Tukur Dahiru (MBBS), FMCPH, Dip. HSM (Israel) Dept. of Community Medicine, Ahmadu Bello University, Zaria, Nigeria. All Correspondence to: ABSTRACT Dr. Tukur Dahiru While it’s not the intention of the founders of significance testing MBBS, FMCPH, Dip HSM (Israel) and hypothesis testing to have the two ideas intertwined as if DEPT OF COMMUNITY MEDICINE they are complementary, the inconvenient marriage of the two AHMADU BELLO UNIVERSITY, practices into one coherent, convenient, incontrovertible and ZARIA, NIGERIA. misinterpreted practice has dotted our standard statistics Email:[email protected] textbooks and medical journals. This paper examine factors contributing to this practice, traced the historical evolution of the Fisherian and Neyman-Pearsonian schools of hypothesis testing, exposed the fallacies and the uncommon ground and common grounds approach to the problem. Finally, it offers recommendations on what is to be done to remedy the situation. INTRODUCTION The medical journals are replete with P values and tests whether a new nutrition therapy improves nutritional of hypotheses. It is a common practice among medical status of malnourished children. We study a group of researchers to quote whether the test of hypothesis malnourished children treated with the new therapy they carried out is significant or non-significant and and a comparable group treated with old nutritional many researchers get very excited when they discover therapy and find in the new therapy group an a “statistically significant” finding without really improvement of nutritional status by 2 units over the understanding what it means. Additionally, while old therapy group. This finding will obviously, be medical journals are florid of statement such as: welcomed but it is also possible that this finding is “statistical significant”, “unlikely due to chance”, “not purely due to chance. Thus, Fisher saw P value as an significant,” “due to chance”, or notations such as, “P index measuring the strength of evidence against the > 0.05”, “P < 0.05”, the decision on whether to decide null hypothesis (in our examples, the hypothesis that a test of hypothesis is significant or not based on P there is no association between poverty level and value has generated an intense debate among malnutrition or the new therapy does not improve statisticians. It began among founders of statistical nutritional status). To quantify the strength of evidence inference more than 60 years ago1-3. One contributing against null hypothesis “he advocated P < 0.05 (5% factor for this is that the medical literature shows a significance) as a standard level for concluding that strong tendency to accentuate the positive findings; there is evidence against the hypothesis tested, though many researchers would like to report positive findings not as an absolute rule’’ 8. Fisher did not stop there but based on previously reported researches as “non- graded the strength of evidence against null hypothesis. significant results should not take up” journal space4-7. He proposed “if P is between 0.1 and 0.9 there is certainly no reason to suspect the hypothesis tested. If The idea of significance testing was introduced by R.A. it’s below 0.02 it is strongly indicated that the hypothesis Fisher, but over the past six decades its utility, fails to account for the whole of the facts. We shall understanding and interpretation has been not often be astray if we draw a conventional line at misunderstood and generated so much scholarly 0.05’’ 9. Since Fisher made this statement over 60 years writings to remedy the situation3. Alongside the ago, 0.05 cut-off point has been used by medical statistical test of hypothesis is the P value, which researchers worldwide and has become ritualistic to similarly, its meaning and interpretation has been use 0.05 cut-off mark as if other cut-off points cannot misused. To delve well into the subject matter, a short be used. Through the 1960s it was a standard practice history of the evolution of statistical test of hypothesis in many fields to report P values with the star attached is warranted to clear some misunderstanding. to indicate P < 0.05 and two stars to indicate P < 0.01. Occasionally three stars were used to indicate P A Brief History of P Value and Significance < 0.001. While Fisher developed this practice of Testing quantifying the strength of evidence against null Significance testing evolved from the idea and practice hypothesis some eminent statisticians where not of the eminent statistician, R.A. Fisher in the 1930s. accustomed to the subjective interpretation inherent in His idea is simple: suppose we found an association the method 7. This led Jerzy Neyman and Egon between poverty level and malnutrition among children Pearson to propose a new approach which they called under the age of five years. This is a finding, but could “Hypothesis tests”. They argued that there were two it be a chance finding? Or perhaps we want to evaluate Annals of Ibadan Postgraduate Medicine. Vol.6 No.1 June, 2008 21 types of error that could be made in interpreting the tables. Now calculating exact P values is easy (i.e., the results of an experiment as shown in Table 1. computer does it) and so the investigator can report (P = 0.04) and leave it to the reader to (determine its The truth significance)”11. Resultof experiment Null hypothesis true Null hypothesis false Hypothesis Tests A statistical test provides a mechanism for making Reject null hypothesis Type I error rate(α) Power = 1-β quantitative decisions about a process or processes. Accept null hypothesis Correct decision Type II error rate (β) The purpose is to make inferences about population parameter by analyzing differences between observed Table 1.Errors associated with results of experiment. sample statistic and the results one expects to obtain if some underlying assumption is true. This comparison The outcome of the hypothesis test is one of two: to may be a single observed value versus some reject one hypothesis and to accept the other. Adopting hypothesized quantity or it may be between two or this practice exposes one to two types of errors: reject more related or unrelated groups. The choice of null hypothesis when it should be accepted (i.e., the statistical test depends on the nature of the data and two therapies differ when they are actually the same, the study design. also known as a false-positive result, a type I error or an alpha error) or accept null hypothesis when it should Neyman and Pearson proposed this process to have rejected (i.e. concluding that they are the same circumvent Fisher’s subjective practice of assessing when in fact they differ, also known as a false-negative strength of evidence against the null effect. In its usual result, type II error or a beta error). form, two hypotheses are put forward: a null hypothesis (usually a statement of null effect) and an What does P value Mean? alternative hypothesis (usually the opposite of null The P value is defined as the probability under the hypothesis). Based on the outcome of the hypothesis assumption of no effect or no difference (null test one hypothesis is rejected and accept the other hypothesis), of obtaining a result equal to or more based on a previously predetermined arbitrary extreme than what was actually observed. The P stands benchmark. This bench mark is designated the P value. for probability and measures how likely it is that any However, one runs into making an error: one may observed difference between groups is due to chance. reject one hypothesis when in fact it should be accepted Being a probability, P can take any value between 0 and vise versa. There is type I error or á error (i.e., and 1. Values close to 0 indicate that the observed there was no difference but really there was) and type difference is unlikely to be due to chance, whereas a P II error or â error (i.e., when there was difference value close to 1 suggests no difference between the when actually there was none). In its simple format, groups other than due to chance. Thus, it is common testing hypothesis involves the following steps: in medical journals to see adjectives such as “highly significant” or “very significant” after quoting the P 1. Identify null and alternative hypotheses. value depending on how close to zero the value is. 2. Determine the appropriate test statistic and its distribution under the assumption that the null Before the advent of computers and statistical hypothesis is true. software, researchers depended on tabulated values 3. Specify the significance level and determine the of P to make decisions. This practice is now obsolete corresponding critical value of the test statistic and the use of exact P value is much preferred. under the assumption that null hypothesis is true. Statistical software can give the exact P value and allows 4. Calculate the test statistic from the data. appreciation of the range of values that P can take up Having discussed P value and hypothesis testing, between 0 and 1. Briefly, for example, weights of 18 fallacies of hypothesis testing and P value are now subjects were taken from a community to determine looked into. if their body weight is ideal (i.e. 100kg). Using student’s t test, t turned out to be 3.76 at 17 degree of freedom. Fallacies of Hypothesis Testing Comparing tstat with the tabulated values, t= 3.26 is In a paper I submitted for publication in one of the more than the critical value of 2.11 at p=0.05 and widely read medical journals in Nigeria, one of the therefore falls in the rejection zone.
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