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The following table defines the possible outcomes when testing multiple null hypotheses. Suppose we have a number m of null hypotheses, denoted by: H 1, H 2, ..., H m. Using a statistical test, we reject the null hypothesis if the test is declared significant. We do not reject the null hypothesis if the test is non-significant.
A few online and downloadable software tools help automate the ACH process. These programs leave a visual trail of evidence and allow the analyst to weigh evidence. PARC ACH 2.0 [17] [18] was developed by Palo Alto Research Center (PARC) in collaboration with Richards J. Heuer, Jr. It is a standard ACH program that allows analysts to enter ...
The sign test is a special case of the binomial test where the probability of success under the null hypothesis is p=0.5. Thus, the sign test can be performed using the binomial test, which is provided in most statistical software programs. On-line calculators for the sign test can be founded by searching for "sign test calculator".
An example of Neyman–Pearson hypothesis testing (or null hypothesis statistical significance testing) can be made by a change to the radioactive suitcase example. If the "suitcase" is actually a shielded container for the transportation of radioactive material, then a test might be used to select among three hypotheses: no radioactive source ...
Median test (also Mood’s median-test, Westenberg-Mood median test or Brown-Mood median test) is a special case of Pearson's chi-squared test. It is a nonparametric test that tests the null hypothesis that the medians of the populations from which two or more samples are drawn are identical. The data in each sample are assigned to two groups ...
Orange, an open-source data mining and machine learning software suite. Python, an open-source programming language widely used in data mining and machine learning. R, an open-source programming language for statistical computing and graphics. Together with Python one of the most popular languages for data science.
In statistics, Dunnett's test is a multiple comparison procedure [1] developed by Canadian statistician Charles Dunnett [2] to compare each of a number of treatments with a single control. [ 3 ] [ 4 ] Multiple comparisons to a control are also referred to as many-to-one comparisons.
The best example of the plug-in principle, the bootstrapping method. Bootstrapping is a statistical method for estimating the sampling distribution of an estimator by sampling with replacement from the original sample, most often with the purpose of deriving robust estimates of standard errors and confidence intervals of a population parameter like a mean, median, proportion, odds ratio ...