is the probability of obtaining the same or more extreme data assuming the null hypothesis of no effect; p-values are generally (but arbitrarily) considered significant if p 0.05.

The probability of obtaining a difference between sample estimates as large as the observed difference if the null hypothesis is true.

a measure of how much evidence we have against the null hypotheses

a measure of how much evidence you have against the null hypothesis

a measure that provides a sense of the strength of the evidence against what is expected by chance

See Statistically Significant.

The probability of a Type I error

The probability of getting a particular value of a test statistic, or a larger value, when the null hypothesis is true.

The probability of observing in a sample an effect at least as unlikely as that observed when in fact there is no effect in the population.

This represents a probability that, given a database of a particular size, random sequences score higher than a value X. P-values are generated by the BLAST algorithm that has been integrated into SMART.

a probability value that is reported in experiments such as clinical trials. The p-value indicates how likely it is that the result obtained by the experiment is due to chance alone. A p-value of less than .05 is considered statistically significant, that is, not likely to be due to chance alone.

The p (probability) value shows how likely it is that the results of an experiment have occurred randomly (by chance). For example, if the p value is 0.05 this means that there is a 5% chance that the results could have been produced by random factors rather than by the intervention.

A statistics term. A measure of probability that a difference between groups during an experiment happened by chance. For example, a p-value of .01 (p = .01) means there is a 1 in 100 chance the result occurred by chance. The lower the p-value, the more likely it is that the difference between groups was caused by treatment.

The p-value is the probability that a test statistic would assume a value greater than or equal to the observed value, i.e. the probability of observing the given sample result under the assumption that the hypothesis is true. In other words, the p-value is the smallest significance level at which the null hypothesis would be rejected for the given sample. Note that the p-value does not measure the probability that the hypothesis is true. Also note that the p-value is not the probability of rejecting a true hypothesis because this probability is determined by the chosen significance level Î±.

the probability value, which is the estimated probability that a hypothesis is found to be true when it actually is not. In this study, it is the probability that a trend is found when there is in fact no true trend (i.e. that random variation in concentration alone could produce the observed behavior). A lower p-value indicates greater confidence in a given conclusion.

A term for the probability that an event will occur. In common research terminology, the p-value of an inferential statistic is the probability that an observed outcome—and more extreme outcomes—could have happened by chance under the null hypothesis that there was no effect. Small p-values such as five times in a hundred (p = .05) are used benchmarks to distinguish effects that could have happened by chance from effects unlikely to happen by chance.

A p-value shows the probability that sample data do not adequately represent the population from which they were drawn. (from the BRFSS site http://www.cdc.gov/brfss)

Measure of statistical significance - lower the value the more statistically probably the event is accurate/real

A measure of the significance of a statistical test. It can be viewed as a measure of how well as model is performing. P-values of less than 0.05 are generally considered to be an indicator that a statistical model is significant.

The probability of an observed result happening by chance under the null hypothesis.

P-value was calculated according to the hypergeometric distribution: the chance of getting x or more hits for a annotation when randomly picking a set of size g proteins out of a database of d proteins, given there are k proteins in the database with this annotation is

The probability that the difference between two sets of statistics is due to randomness (see Significance test) 53

When comparing two treatments in a trial, the probability that the results obtained would have occurred had there truly been no difference between treatments. The p-value is a measure of the "false positive rate" in a clinical trial.

The probability of observing a result as extreme as or more extreme than the one actually observed from chance alone (i.e., if the null hypothesis is true).

obtained significance level for a statistical test. The p-value represents the likelihood, under the assumption that the null hypothesis is true, that the data would yield the obtained results.

In statistical hypothesis testing, the p-value is the probability of obtaining a result at least as extreme as that obtained, assuming the truth of the null hypothesis that the finding was the result of chance alone. The fact that p-values are based on this assumption is crucial to their correct interpretation.