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 ?
Dentro de ese proceso de valoracion critica de la evidencia cientifica es crucial conocer y comprender el proceso de contraste de hipotesis estadisticas mediante la ejecucion de la prueba de significacion de la hipotesis nula (Null Hypothesis Significance Testing [NHST]), sobre todo teniendo en cuenta que en el ambito de la psicologia el procedimiento de la NHST es la tecnica por excelencia en el analisis de datos (Cumming et al.
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Use the Geoda software to carry out the significance testing of new urbanization's inclusive developmental levels of 11 prefecture-level cities in 2005, 2008, 2011 and 2014.
In early 2015, the psychology journal Basic and Applied Social Psychology announced its ban on the use of "Null Hypothesis Significance Testing Procedure" (NHSTP), because of its invalidity.
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.
This is the same idea as if you were to conduct null hypothesis significance testing for a study and then after you saw what the p-value was, you determined what your significance level ([alpha]) should be.
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 usually involves a corrupt form of scientific method and, at best is of trivial importance (Carver, 1978).
Statistical significance testing was completed and represented results have a 95 percent confidence level.

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