A ‘research hypothesis’ is a plausible explanation for a phenomenon or observation. It should logically provide one or more predictions, which can then be tested. Testing of predictions could involve making further observation and measurement, or could assess whether the predictions are compatible with other established facts related to the current observation. A research hypothesis is a deductive approach to a problem. It generates predictions about the problem, which can be tested. In the formal testing of predictions using statistical methods, a ‘statistical hypothesis’ is defined that provides a rigorous test of a prediction.

This topic covers: Null hypothesis, Type I Error, Type II Error, Specificity, Sensitivity, Level of significance / Tests of significance, Power of a Hypothesis Test, P value, Statistical estimation of confidence intervals, One Sided Confidence Interval, Two Sided Confidence Interval, P Value Confidence Interval, Binomial Confidence Interval, Degrees of freedom.

Basics of testing hypothesis – Null hypothesis

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