Editing Data dredging (section)

==Examples ==

=== In meteorology and epidemiology ===
In [[meteorology]], hypotheses are often formulated using weather data up to the present and tested against future weather data, which ensures that, even subconsciously, future data could not influence the formulation of the hypothesis. Of course, such a discipline necessitates waiting for new data to come in, to show the formulated theory's [[predictive power]] versus the [[null hypothesis]]. This process ensures that no one can accuse the researcher of hand-tailoring the [[predictive modelling|predictive model]] to the data on hand, since the upcoming weather is not yet available.

As another example, suppose that observers note that a particular town appears to have a [[cancer cluster]], but lack a firm hypothesis of why this is so. However, they have access to a large amount of [[demographic data]] about the town and surrounding area, containing measurements for the area of hundreds or thousands of different variables, mostly uncorrelated. Even if all these variables are independent of the cancer incidence rate, it is highly likely that at least one variable correlates significantly with the cancer rate across the area. While this may suggest a hypothesis, further testing using the same variables but with data from a different location is needed to confirm. Note that a ''p''-value of 0.01 suggests that 1% of the time a result at least that extreme would be obtained by chance; if hundreds or thousands of hypotheses (with mutually relatively uncorrelated independent variables) are tested, then one is likely to obtain a ''p''-value less than 0.01 for many null hypotheses.