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Much of classical hypothesis testing, for example, was based on the assumed normality of the data.
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While hypothesis testing was popularized early in the 20th century, evidence of its use can be found much earlier.
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It is noted the importance of measurement and analysis, focusing on the (difficult to achieve) goal of objective research or statistical hypothesis testing.
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Statistical hypothesis testing is founded on the assessment of differences and similarities between frequency distributions.
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Hypothesis testing is the art of testing whether a variation between two sample distributions can be explained by chance or not.
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These include hypothesis testing, the partitioning of sums of squares, experimental techniques and the additive model.
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It forms a major component in modern statistical hypothesis testing.
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One model they produced explained hypothesis testing behavior of subjects learning very simple classifications (concepts) in the standard trial-by-trial procedures that overtaxed memory.
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This hybrid is in turn criticized as incorrect and incoherentsee statistical hypothesis testing.
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Statistical hypothesis testing is sometimes called confirmatory data analysis, in contrast to exploratory data analysis, which may not have pre-specified hypotheses.
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