Hypothesis testing is an essential procedure in statistics , it evaluates two mutually exclusive statements about a population to determine which statement is best supported by data i.e which statement is statistically significant. But, why do we need hypothesis testing ? that is because we are making our conclusion about a population basis the sample data – hypothesis tests helps assess the likelihood of this possibility that the sample data is representative of the population data. This itself makes hypothesis testing very significant as it helps us assess the population parameters using the sample statistics.
Hypothesis tests can be used across Define, Measure, Analyze and Improve phase of an improvement project
Define Phase : To test whether the target set is significantly different from the baseline performance.
Measure phase : Understanding the likelihood that a data sample comes from a population that follows a given probability distribution (i.e. normal, exponential, uniform, etc.)
Analyse Phase : For screening potential causes. Evaluating several process factors (process inputs, or x’s) in a designed experiment to understand which factors are significant to a given output, and which are not.
Improve phase : Evaluating a proposed process improvement, using pilot study output, to see if its effect is statistically significant, or if the same improvement could have occurred by random chance.
Thanks
Jisha Nair