A formal method for choosing between two explanations of a statistical connection in a sample is null hypothesis testing. The null hypothesis is one possible interpretation (typically represented by the sign H0 and written as "H-naught"). According to this theory, it has no connection with the population, and the association in the study is the result of sampling error. Unofficially, the sample association "occurred by chance," according to the null hypothesis. The alternate hypothesis is the other interpretation (often symbolized as H1). This is the contention that a correlation existed overall and that the correlation in the sample accurately captures this connection. In hypothesis testing, the whole set of respondents is transformed into a single number, called a statistical test. Some test numbers are presumably already recognisable to you. In hypothesis testing, the tails at either extreme of a distribution curve are referred to as tails. Obtaining all of the data samples and converting them to a single number is often what is meant by a test statistic in hypothesis testing.
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