12 relations: Data dredging, Deductive reasoning, Dependent and independent variables, Economics, Faulty generalization, Overfitting, Psychology, Regression analysis, Social science, Statistical significance, Type I and type II errors, Variance.
Data dredging
Data dredging (also data fishing, data snooping, and '''''p'''''-hacking) is the use of data mining to uncover patterns in data that can be presented as statistically significant, without first devising a specific hypothesis as to the underlying causality.
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Deductive reasoning
Deductive reasoning, also deductive logic, logical deduction is the process of reasoning from one or more statements (premises) to reach a logically certain conclusion.
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Dependent and independent variables
In mathematical modeling, statistical modeling and experimental sciences, the values of dependent variables depend on the values of independent variables.
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Economics
Economics is the social science that studies the production, distribution, and consumption of goods and services.
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Faulty generalization
A faulty generalization is a conclusion about all or many instances of a phenomenon that has been reached on the basis of just one or just a few instances of that phenomenon.
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Overfitting
In statistics, overfitting is "the production of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit additional data or predict future observations reliably".
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Psychology
Psychology is the science of behavior and mind, including conscious and unconscious phenomena, as well as feeling and thought.
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Regression analysis
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables.
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Social science
Social science is a major category of academic disciplines, concerned with society and the relationships among individuals within a society.
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Statistical significance
In statistical hypothesis testing, a result has statistical significance when it is very unlikely to have occurred given the null hypothesis.
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Type I and type II errors
In statistical hypothesis testing, a type I error is the rejection of a true null hypothesis (also known as a "false positive" finding), while a type II error is failing to reject a false null hypothesis (also known as a "false negative" finding).
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Variance
In probability theory and statistics, variance is the expectation of the squared deviation of a random variable from its mean.
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