hypothesis testing


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hypothesis testing

[hī′päth·ə·səs ‚test·iŋ]
(statistics)
The branch of statistics which considers the problem of choosing between two actions on the basis of the observed value of a random variable whose distribution depends on a parameter, the value of which would indicate the correct action.
References in periodicals archive ?
Sequential hypothesis testing [16] is a suitable technique for making a decision in real-time.
Under different statistical distributions of samples, the specific hypothesis testing problems are different.
Sample size for hypothesis testing of the primary question is a function of the type I error rate or significance level (usually 0.05 or smaller), power (1-[beta], 80% or larger), minimal clinically significant difference in primary outcome by treatments (set by investigators), and measure of variability (usually from pilot or related studies) in primary outcome.
In Table 4, which has been named as hypothesis testing, inferential statistics known as Pearson correlation coefficient (R) is used.
Consequently, hypothesis testing based on fuzzy test statistic and fuzzy critical values that is described above is more realistic and provides more benefits when value of the test statistic is very near to the quantile of the test statistic.
Figure 6 Bayesian Decision Rule O" R > 1, accept state A O" R < 1, accept state B Classical hypothesis testing can be used in situations where plausible inferences about a population can be made.
From the hypothesis testing on the retention rate there is no statistical evidence that the (M) classes have higher retention rates than the (E) classes.
noninformative hypothesis testing and explain why, whenever possible,
For hypothesis testing, the recommended estimator is
There, proponents of intelligent design proposed changing the definition of science from "seeking natural explanations for what we observe around us"--the current definition in the state's science standards--to "continuing investigation that uses observation, hypothesis testing, measurement, experimentation, logical argument and theory building to lead to more adequate explanations of natural phenomena."
We also discussed and reinforced other related concepts related to regression analysis, including interpreting and understanding scatterplots, correlation, hypothesis testing, assumptions, confidence intervals and p-values.