The P value gives the probability that the null hypothesis is correct.

The P value is a quantitative estimate of the probability that the observed difference between two groups could have happened by chance alone.

In statistics, a value very close to the probability of making a Type I error, the error of rejecting a true null hypothesis. A null hypothesis means no statistical significance, with any differences in populations or samples being due to chance. To make a Type I error is to find statistical significanc e when it is not there, as in mistaking chance results for treatment effects. For findings to be statistically significant, the probability of making a Type I error must be small, the smaller the better. The traditional cutoff value is 5 percent. If a p value is 5 percent or less (p=0.05), the null hypothesis can be rejected and there is statistical significance.

The probability that a result occurred by chance. The lower the p value, the less likely the result was due to chance. A p value ·.05 is usually considered statistically significant.

Hand-in-hand with the test statistic is the p value which indicates the probability of getting the characteristics observed in a sample if the null hypothesis were true.

Probability value. A number that reflects the likelihood that statistical results have occurred by chance. Results with p values equal to or less than .05, .01 or .001 are labeled as statistically significant. Also known as level of significance.

The probability that the difference(s) observed between two or more groups in a study would occurred if there were no differences between the groups other than those created by random selection. The assumption underlying the p-value is the null hypothesis.

Statistical data, such as data from human tests of a candidate drug, are often accompanied by a P value, which is the mathematical probability that the data are the result of random chance. Data with a low P value (less than or equal to 0.05) are said to be "statistically significant." For example, a P value of 0.05 means that there is a 1 in 20 chance that the data is the result of random chance.

a measure of the probability with which a particular data set, or one even more extreme, would have occured just by chance if the null hypothesis were indeed true

a probability associated with a hypothesized test

a statistical term which tells us the percentage value that the results were due to pure random chance and the percentage value that the results were actually due to SinEcch's ability to really reduce bruising and swelling

The probability that a given result obtained in a statistical test could have occurred by chance alone rather than because of a hypothesized relationship. For example, if a correlation coefficient has .05, we infer that the observed correlation is not likely to have been a random occurrence as the value suggests that particular correlation would be obtained by chance alone fewer than 5 times out of 100. (Please consult a standard statistics text for computational formulas and further explanation.)

The symbol P (or p) means probability. The symbol p followed by the mathematical symbol (less than) 0.05 is used to indicate that the result could be expected to occur by chance less than five times in 100, or once out of 20. Another way of expressing this P value is to say that the result is “significant at the 5% level”. The smaller the P value, the less likely the results happened by chance. The P value is closely linked to confidence intervals.

The p value is the probability of having observed our data (or more extreme data) when the null hypothesis is true. In other words, if the null hypothesis is true, the p value gives the probability of observing our data (or more extreme) by chance, so it can be thought of as a measure of strength in the belief of the null hypothesis. To illustrate: we sample a classroom of 30 children to test the null hypothesis that the population of boys and girls are on average of equal heights. A p value of 0.01 suggests that the probability of collecting the observed heights of the 30 children (or with a greater height difference between the boys and girls) is 0.01 when the overall population of boys and girls are truly of equal height.

The findings of a study may be just an unusual fluke. Calculating the p value can determine whether or not the results of the study are likely to be a fluke or not. The p (probability) value shows whether or not the result could have been caused by chance. If the p value is less than 0.05, then the result is not due to chance. A result with a p value of less than 0.05 is statistically significant. The 0.05 level is equal to odds of 19 to 1 (or a 1 in 20 chance). (See also confidence interval, power, and probability).

A P-value indicates how unusual a computed test statistic is compared with what would be expected under the null hypothesis. A small value indicates that the null hypothesis should be rejected at any significance level above the calculated value. For example, if the P value equals .0246, we would reject the null hypothesis at the 5% significance level, but would not reject it at the 1% significance level. P values are printed in procedures such as Multiple Regression to determine whether the estimated coefficients are significantly different than zero.

The probability of an alignment occurring with the score in question or better.

The probability that the null hypothesis is true, calculated using inferential statistics. The statistical test returns a P value that can be compared with an acceptbale or expected level (alpha).

A confidence coefficent or a statistical value used in the multiple comparison procedure for comparing several treatments with a control.