statistical significance

(redirected from Significance testing)

sta·tis·ti·cal sig·nif·i·cance

(stă-tis'ti-kăl sig-nif'i-kăns),
Statistical methods allow an estimate to be made of the probability of the observed degree of association between variables, and from this the statistical significance can be expressed, commonly in terms of the p value.

statistical significance

Etymology: L, status, condition, significare, to signify
an interpretation of statistical data that indicates that an occurrence was probably the result of a causative factor and not simply a chance result. Statistical significance at the 1% level indicates a 1 in 100 probability that a result can be ascribed to chance.

statistical significance

A term used in statistical analysis when a hypothesis is rejected. As a general rule, the non plus minimum significance level is 5%—i.e., it is said to be significant at the 5% level—which means that when the null hypothesis is true, there is only a 1-in-20 chance of rejecting it.

statistical significance

Significance Statistics A statement of the probability that an observation represents a true causal relationship and not a chance occurrence; the probability that an event or difference occurred as the result of an intervention–eg, a vaccine, rather than by chance alone; this probability is determined by using statistical tests to evaluate collected data. See Significance.

sta·tis·ti·cal sig·nif·i·cance

(stă-tis'ti-kăl sig-nif'i-kăns)
Statistical methods that allow an estimate to be made of the probability of the observed degree of association between variables, and from this the statistical significance can be expressed, commonly in terms of the p value.

statistical significance,

n a difference of such magnitude between two statistics, computed from separate samples, that the probability of the value obtained will not occur by chance alone with significant frequency and hence can be attributed to something other than chance. In modern investigation the generally accepted value for significance must have a probability of occurrence by chance factors equal to or less than five times in 100 (
p < 0.05).="" other="" significance="" levels="" commonly="" used="" are="" as="" follows:="" less="" than="" one="" chance="" in="" 100="">
p > 0.01), less than five chances in 1000 (
p < 0.005),="" and="" less="" than="" one="" chance="" in="" 1000="">
p <>

statistical

pertaining to or emanating from statistics.

statistical efficiency
between-test comparisons are based on the ratio of sample sizes required for the tests to have equal probabilities of detecting the same false null hypothesis; the more efficient test will have the smaller sample size.
statistical methods
procedures for collecting, classifying, summarizing, analyzing and making conclusions about, data. See also regression (4), path analysis, factor, discriminant analysis.
statistical significance
References in periodicals archive ?
Its tool-set includes a full range of descriptive statistics, significance testing, regression analysis, time-series models, predictive analytics, and more.
A case against statistical significance testing, revisited.
Investigation revealed that the third sub-hypothesis significance testing the value of 0.
Citing another author, we would say that significance testing "does not tell us what we want to know, and we so much want to know what we want to know that, out of desperation, we nevertheless believe that it does
Volume 2 covers hypothesis testing and inference, with readings grouped in sections on history, the reasoning processes in hypothesis testing, null hypothesis significance testing, and power and effect size.
Significance testing like this is widely used in the sciences.
Beyond Significance Testing: Statistical Reform in The Behavioral Sciences" is a scholarly discussion of significance testing from author Dr.
Statistical significance testing was completed and represented results have a 95 percent confidence level.
For decades, a string of influential psychologists have recommended disposing of null hypothesis significance testing altogether, calling the approach an unscientific ritual that should be replaced by testing specific predictions.
Apart from simplicity the repeated significance testing approach is fraud with problems.
Significance testing was restricted to the use of ANOVA for determining if facility room made a difference in number of suspected MRSA colonies.