It is often seen in medical publications when the zero hypothesis is being tested. This figures the probability (p value) of a type I error which is, that a particular finding is attributable to chance.
It is also essential to be aware of the chances of a type II error which is, accepting the zero hypothesis when it does not utilize. Sample size, point estimate and variability are typical factors that will impact the chances of making these 2 types of errors.
Statistics and causal inference tries to uncover the structure of the data and remove all non-causative explanations for an observed association. The purpose of most, if not all, statistical inference is to discover causal relationships. However it is not in general possible to conclude causality through a standard statistical inference process, it is merely possible to conclude how the observed association among two variables is not because of chance.
Statistics and causal inference procedures do not supply any information about which variable causes the other or whether the apparent connection between both variables is because of another, confounding variable. The explicit research of causation had been first introduced to the statistical sciences in 1986 by Paul Holland. Since then, there has been an explosion of study into the area in a number of disciplines such as computer science, statistics (particularly biostatistics) and economics.
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