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In broad usage, the "practical clinical significance" answers the question, how effective is the intervention or treatment, or how much change does the treatment cause. In terms of testing clinical treatments, practical significance optimally yields quantified information about the importance of a finding, using metrics such as effect size, number needed to treat (NNT), and preventive fraction ...
In null-hypothesis significance testing, the p-value [note 1] is the probability of obtaining test results at least as extreme as the result actually observed, under the assumption that the null hypothesis is correct. [2] [3] A very small p-value means that such an extreme observed outcome would be very unlikely under the null hypothesis.
Frequently used in medical studies is the statistical significance of P < 0.05. [4] The P value is the probability of no effect or no difference (null hypothesis) of obtaining a result essentially equal to what was actually observed. The P stands for probability and measures how likely it is that any observed difference between groups is due to ...
A clinical researcher might report: "in my own experience treatment X does not do well for condition Y". [3] [4] The use of a P value cut-off point of 0.05 was introduced by R.A. Fisher; this led to study results being described as either statistically significant or non-significant. [5]
To determine whether a result is statistically significant, a researcher calculates a p-value, which is the probability of observing an effect of the same magnitude or more extreme given that the null hypothesis is true. [5] [12] The null hypothesis is rejected if the p-value is less than (or equal to) a predetermined level, .
Fisher's test gives exact p-values, but some authors have argued that it is conservative, i.e. that its actual rejection rate is below the nominal significance level. [ 4 ] [ 14 ] [ 15 ] [ 16 ] The apparent contradiction stems from the combination of a discrete statistic with fixed significance levels.
The red dashed line indicates the commonly used significance level of 0.05. If the data collection or analysis were to stop at a point where the p-value happened to fall below the significance level, a spurious statistically significant difference could be reported.
A one-tailed test, showing the p-value as the size of one tail. In statistical significance testing, a one-tailed test and a two-tailed test are alternative ways of computing the statistical significance of a parameter inferred from a data set, in terms of a test statistic. A two-tailed test is appropriate if the estimated value is greater or ...