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Statistics, Explanatory

Bertram Scheufele


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Explanatory statistics is also called inferential statistics or statistical induction and deals with inferences about the population from the characteristics of a random sample, i.e., with making (probability) statements about usually unknown parameters of a population. For instance, when taking a random sample (e.g., n = 1,000) of television viewers from the population of all TV viewers (e.g., N = 1,000,000), we want to know if the average time of TV viewing in the sample (e.g., 184 minutes/weekday) comes close to the average time in the population from which the sample was taken (→  Statistics, Descriptive ; Sampling, Random ; Generalizability ). Explanatory statistics includes point and interval estimation as well as hypothesis tests for statistical significance. They are based on probability theory (e.g., the work of Richard von Mises, Thomas Bayes, and Pierre-Simon Laplace). On the one hand, probability can be interpreted as the ratio of a favorable outcome and the number of possible outcomes. For instance, if one expects a “6” when throwing a single die once, the probability of getting a “6” is the ratio of this favorable ourcome (n = 1) divided by the possible outcomes (n = 6) – thus p(“6”) = 1/6. Throwing a single die is an example of a random experiment. It is called random since we do not know the outcome before having thrown the die. On the other hand, we can assume ... log in or subscribe to read full text

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