Statistical thinking pervades the empirical sciences. It is used to provide principles of initial description, concept formation, model development, observational design, theory development and theory testing, and much more. Some of these activities consist in computing significance tests for statistical hypotheses. Such a hypothesis typically is a statement about a regression coefficient in a linear regression or a relative risk for a chosen life-course event, such as marriage formation or death. The hypothesis can state that the regression coefficient equals zero (or that the relative risk equals 1), implying that the corresponding covariate has no impact on the transition in question and thus does not affect the behavior it represents, or that for all practical purposes the analyst may act as if this were the case. Alternatively the hypothesis may predict the sign of the coefficient, for example that higher education leads to lower marriage rates, ceteris paribus, as argued by some...
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References and Further Reading
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Hoem, J.M. (2011). Statistical Significance. In: Lovric, M. (eds) International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_84
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