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An erosion gully in Australia caused by rabbits, an unintended consequence of their introduction as game animals. In the social sciences, unintended consequences (sometimes unanticipated consequences or unforeseen consequences, more colloquially called knock-on effects) are outcomes of a purposeful action that are not intended or foreseen.
A reconstruction of the skull purportedly belonging to the Piltdown Man, a long-lasting case of scientific misconduct. Scientific misconduct is the violation of the standard codes of scholarly conduct and ethical behavior in the publication of professional scientific research.
In addition to causing numerical problems, imperfect collinearity makes precise estimation of variables difficult. In other words, highly correlated variables lead to poor estimates and large standard errors. As an example, say that we notice Alice wears her boots whenever it is raining and that there are only puddles when it rains.
Appeal to consequences, also known as argumentum ad consequentiam (Latin for "argument to the consequence"), is an argument that concludes a hypothesis (typically a belief) to be either true or false based on whether the premise leads to desirable or undesirable consequences. [1]
Manifest functions are the consequences that people see, observe or even expect. It is explicitly stated and understood by the participants in the relevant action. The manifest function of a rain dance, used as an example by Merton in his 1957 Social Theory and Social Structure, is to produce rain, and this outcome is intended and desired by people participating in the ritual.
Taleb's "black swan theory" (which differs from the earlier philosophical versions of the problem) refers only to statistically unexpected events of large magnitude and consequence and their dominant role in history. Such events, considered extreme outliers, collectively play vastly larger roles than regular occurrences.
Sometimes missing values are caused by the researcher—for example, when data collection is done improperly or mistakes are made in data entry. [2] These forms of missingness take different types, with different impacts on the validity of conclusions from research: Missing completely at random, missing at random, and missing not at random.
One way to model spillover effects is a binary indicator for whether an immediate neighbor was also treated, as in the example above. One can also posit spillover effects that depend on the number of immediate neighbors that were also treated, also known as k-level effects.