Is it possible to have a p value of 1




















It is a probability and, as a probability, it ranges from Can a p-value be greater than 1? Why or why not? Kate M. Mar 21, It is an accepted fact among statisticians of the inadequacy of P value as a sole standard judgment in the analysis of clinical trials Just as hypothesis testing is not devoid of caveats so also P values. Some of these are exposed below. As has been said earlier, it was the practice of Fisher to assign P the value of 0.

This problem is more serious when several tests of hypothesis involving several variables were carried without using the appropriate statistical test, e. Statistical significance result does not translate into clinical importance. A large study can detect a small, clinically unimportant finding. Chance is rarely the most important issue. Remember that when conducting a research a questionnaire is usually administered to participants.

This questionnaire in most instances collect large amount of information from several variables included in the questionnaire.

The manner in which the questions where asked and manner they were answered are important sources of errors systematic error which are difficult to measure. Effect size. It is a usual research objective to detect a difference between two drugs, procedures or programmes.

Several statistics are employed to measure the magnitude of effect produced by these interventions. Two problems are encountered: the use of appropriate index for measuring the effect and secondly size of the effect.

A 7kg or 10 mmHg difference will have a lower P value and more likely to be significant than a 2-kg or 4 mmHg difference. Size of sample. The larger the sample the more likely a difference to be detected. Further, a 7 kg difference in a study with participants will give a lower P value than 7 kg difference observed in a study involving participants in each group.

Spread of the data. The spread of observations in a data set is measured commonly with standard deviation. The bigger the standard deviation, the more the spread of observations and the lower the P value. This marriage of inconvenience further deepened the confusion and misunderstanding of the Fisherian and Neyman-Pearson schools. The combination of Fisherian and N-P thoughts as exemplified in the above statements did not shed light on correct interpretation of statistical test of hypothesis and p-value.

The hybrid of the two schools as often read in medical journals and textbooks of statistics makes it as if the two schools were and are compatible as a single coherent method of statistical inference 4 , 23 , Goodman commented on P—value and confidence interval approach in statistical inference and its ability to solve the problem. Thus, a common ground was needed and the combination of P value and confidence intervals provided the much needed common ground. Before proceeding, we should briefly understand what confidence intervals CIs means having gone through what p-values and hypothesis testing mean.

Suppose that we have two diets A and B given to two groups of malnourished children. An 8-kg increase in body weight was observed among children on diet A while a 3-kg increase in body weights was observed on diet B.

The effect in weight increase is therefore 5kg on average. But it is obvious that the increase might be less than 3kg and also more than 8kg, thus a range can be represented and the chance associated with this range under the confidence intervals. In the s, a number of British statisticians tried to promote the use of this common ground approach in presenting statistical analysis 16 , 17 , They encouraged the combine presentation of P value and confidence intervals.

The use of confidence intervals in addressing hypothesis testing is one of the four popular methods journal editors and eminent statisticians have issued statements supporting its use The Task Force suggested,.

Be sure to include sufficient descriptive statistics [e. Jonathan Sterne and Davey Smith came up with their suggested guidelines for reporting statistical analysis as shown in the box 21 :.

Interpretation of confidence intervals should focus on the implication clinical importance of the range of values in the interval.

When there is a meaningful null hypothesis, the strength of evidence against it should be indexed by the P value. The smaller the P value, the stronger is the evidence. While it is impossible to reduce substantially the amount of data dredging that is carried out, authors should take a very skeptical view of subgroup analyses in clinical trials and observational studies.

The strength of the evidence for interaction-that effects really differ between subgroups — should always be presented. Claims made on the basis of subgroup findings should be even more tempered than claims made about main effects. In observational studies it should be remembered that considerations of confounding and bias are at least as important as the issues discussed in this paper.

Since the s when British statisticians championed the use of confidence intervals, journal after journal are issuing statements regarding its use. In an editorial in Clinical Chemistry, it read as follows,. The confidence interval reflects the precision of the sample values in terms of their standard deviation and the sample size ….. On the final note, it is important to know why it is statistically superior to use P value and confidence intervals rather than P value and hypothesis testing:.

Confidence intervals emphasize the importance of estimation over hypothesis testing. It is more informative to quote the magnitude of the size of effect rather than adopting the significantnonsignificant hypothesis testing. The width of the CIs provides a measure of the reliability or precision of the estimate. Confidence intervals makes it far easier to determine whether a finding has any substantive e.

How small is small enough? But the threshold depends on your field of study — some fields prefer thresholds of 0. P- values of statistical tests are usually reported in the results section of a research paper, along with the key information needed for readers to put the p -values in context — for example, correlation coefficient in a linear regression , or the average difference between treatment groups in a t -test.

P -values are often interpreted as your risk of rejecting the null hypothesis of your test when the null hypothesis is actually true. In reality, the risk of rejecting the null hypothesis is often higher than the p -value, especially when looking at a single study or when using small sample sizes.

This is because the smaller your frame of reference, the greater the chance that you stumble across a statistically significant pattern completely by accident. P -values are also often interpreted as supporting or refuting the alternative hypothesis. This is not the case. The p -value can only tell you whether or not the null hypothesis is supported. It cannot tell you whether your alternative hypothesis is true, or why.

A p -value , or probability value, is a number describing how likely it is that your data would have occurred under the null hypothesis of your statistical test. P -values are usually automatically calculated by the program you use to perform your statistical test. They can also be estimated using p -value tables for the relevant test statistic. P -values are calculated from the null distribution of the test statistic. They tell you how often a test statistic is expected to occur under the null hypothesis of the statistical test, based on where it falls in the null distribution.

If the test statistic is far from the mean of the null distribution, then the p -value will be small, showing that the test statistic is not likely to have occurred under the null hypothesis. Active Oldest Votes. Improve this answer. Why discrete suddenly? The question was not about what is assumed when deriving the distribution of the statistic, but what it might mean when a p-value of 1 happened with a particular data set which data set is not itself supplied.

One possible cause is discrete data resulting in a mean difference of exactly 0; if so, there's nothing further to explain. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown.

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