If you are unfamiliar with the theory of tests of statistical significance it is important to first read the section on formal hypothesis testing as it introduces many of the key concepts that determine how data exploration occurs. Of course, academic and commercial research make use of both approaches. Data exploration is prevalent in commercial research. The formal hypothesis testing approach is prevalent in academic research.

To search through large quantities of data and identify interesting patterns ( data exploration).

To test hypotheses that were formulated at the time the research was designed ( formal hypothesis testing).Statistical tests are used in two quite different ways: These rules of thumb are variously known as tests of statistical significance, statistical tests, hypothesis tests, and significance tests. Statisticians have developed a variety of rules of thumb to help distinguish between results that are reflective of the population and results that are not. For example, even though there are more than 300 million Americans, you might only interview 200 of them in a particular survey, it is inevitable that the results from these 200 will differ from the results obtained if all 300 million were interviewed. Customer databases typically only contain a subset of all the potential customers for an organization.Īnalyses based on such subsets will usually produce results that are different to the results that would have been obtained if all possible observations in the population had been included. Surveys, for example, typically involve collecting data from a sample of the total population. Most analyses are not based on all the relevant data.

Testing for assumptions data dredging