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Critical thinkingArticle: Dienes, Z, 2011Bayesian Versus orthodox statistics: which side are you on?doi: 10.1177/1745691611406920 The orthodox logic of statistics, starts from the assumption that probabilities are long-run relative frequencies.A long-run relative frequency requires an indefinitely large series of events that constitutes the collective probability of some property (q) occurring is then the proportion of events in the collective with property q.The probability applies to the whole collective, not to any one person.One person may belong to two different collectives that have different probabilitiesLong run relative frequencies do not apply to the truth of individual theories because theories are not collectives. They are just true or false.Thus, when using this approach to probability, the null hypothesis of no population difference between two particular conditions cannot be assigned a probability.Given both a theory and a decision procedure, one can determine a long-run relative frequency with which certain data might be obtained. We can symbolize this as P(data| theory and decision procedure).The logic of Neyman Pearson (orthodox) statistics is to adopt decision procedures with known long-term error rates and then control those errors at acceptable levels.Alpha: the error rate for false positives, the significance levelBeta: the error...
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WSRt, critical thinking - a summary of all articles needed in the fourth block of second year psychology at the uva
WSRt, critical thinking - a summary of all articles needed in the fourth block of second year psychology at the uva
This is a summary of the articles and reading materials that are needed for the fourth block in the course WSR-t. This course is given to second year psychology students at the Uva. The course is about thinking critically about how scientific research is done and how this could be done differently.
mistake kuqelis contributed on 20-06-2021 14:58
I think the part you mentioned about data and theory might be incorrect check this out:
The probability of a theory being true given data can be symbolized as P(theory | data), and that is what many of us would like to know. This is the inverse of P(data | theory), which is what orthodox statistics tells us.
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