What do we mean by p-value as we place our figures in Table format as we receive after completion of any medical prospective study(may be interventional study or nonintervention Study) ??

In statistics, the p-value is the probability of obtaining results as extreme as the observed results of a statistical hypothesis test, assuming that the null hypothesis is correct.

 The p-value is used as an alternative to rejection points to provide the smallest level of significance at which the null hypothesis would be rejected.

Therefore , a smaller p-value means that there is stronger evidence in favor of the alternative hypothesis.

‎Null Hypothesis · ‎Hypothesis Testing · ‎Statistical Significance

The smaller the p-value, the more certainty there is that the null hypothesis can be rejected.- 

. A p value is used in hypothesis testing to help you support or reject the null hypothesis. The p value is the evidence against a null hypothesis. ... For example, a p value of 0.0254 is 2.54%. This means there is a 2.54% chance your results could be r statistics, the p-value is the probability of obtaining results as extreme as the observed results of a statistical hypothesis test, assuming that the null hypothesis is correct.

... A smaller p-value means that there is stronger evidence in favor of the alternative hypothesis.,Random (i.e. happened by chance).

P > 0.05 is the probability that the null hypothesis is true. 1 minus the P value is the probability that the alternative hypothesis is true.

A statistically significant test result (P ≤ 0.05) means that the test hypothesis is false or should be rejected. A  p value greater than 0.05 means that no effect was observed.

 P > 0.05 is the probability that the null hypothesis is true. 1 minus the P value is the probability that the alternative hypothesis is true. A statistically significant test result (P ≤ 0.05) means that the test hypothesis is false or should be rejected. A p value greater than 0.05 means that no effect was observed.

In statistics, the p-value is the probability of obtaining results as extreme as the observed results of a statistical hypothesis test, assuming that the null hypothesis is correct

, 41 indicates a rejection of the null hypothesis at the 5% significance level, 0 indicates a failure to reject the null hypothesis at the 5% significance level.

 If you are interested in your p-value, just do this: ... The smaller the p-value, the more certainty there is that the null hypothesis can be rejected.

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A smaller p-value means that there is stronger evidence in favor of the alternative hypothesis.

How Is P-Value Calculated?

P-values are calculated using p-value tables or spreadsheets/statistical software. Because different researchers use different levels of significance when examining a question, a reader may sometimes have difficulty comparing results from two different tests. P-values provide a solution to this problem.

For example, if a study comparing returns from two particular assets were undertaken using by different researchers who used the same data but different significance levels, the researchers might come to opposite conclusions regarding whether the assets differ.

To avoid this problem, the researchers could report the p-value of the hypothesis test and allow the reader to interpret the statistical significance themselves. This is called a p-value approach to hypothesis testing.

P-Value Approach to Hypothesis Testing

The p-value approach to hypothesis testing uses the calculated probability to determine whether there is evidence to reject the null hypothesis. The null hypothesis, also known as the conjecture, is the initial claim about a population (or data generating process).