Dienes (2008). Understanding psychology as a science.” – Article summary

A falsifier of a theory is any potential observation statement that would contradict the theory. There are different degrees of falsifiability, as some theories require fewer data points to be falsified than others. In other words, simple theories should be preferred as these theories require fewer data points to be falsified. The greater the universality a theory, the more falsifiable it is. A computational model is a computer simulation of a subject. It has free parameters, numbers that have to be set (e.g. number of neurons used in a computational model of neurons). When using computational models, more than one model will be able to fit the actual data. However, the most falsifiable model that has not been falsified by the data (fits the data) should be used. A theory should only be revised or changed to make it more falsifiable. Making it less falsifiable is ad hoc. Any revision or amendment to the theory should also be falsifiable. FalsifiaStandard statistics are useful in determining probabilities based on the objective probabilities, the long-run relative frequency. This does not, however, give the probability of a hypothesis being correct. Subjective probability refers to the subjective degree of conviction in a hypothesis. The subjective probability is based on a person’s state of mind. Subjective probabilities need to follow the axioms of probability. Bayes’ theorem is a method of getting from one conditional probability (e.g. P(A|B)) to the inverse. The subjective probability of a hypothesis is called the prior. The posterior is how probable the hypothesis is to you after data collection. The probability of obtaining the data given the hypothesis is called the likelihood (e.g. P(D|H). The posterior is proportional to the likelihood times the prior. Bayesian statistics is updating the personal conviction in light of new data.The likelihood principle states that all the information relevant to inference contained in data is...


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Scientific & Statistical Reasoning – Article summary (UNIVERSITY OF AMSTERDAM)

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This bundle contains all the summaries for the course "Scientific & Statistical Reasoning" given at the University of Amsterdam. It contains the following articles:

  • “Borsboom & Cramer (2013). Network analysis: An integrative
...

Scientific & Statistical Reasoning – Summary interim exam 2 (UNIVERSITY OF AMSTERDAM)

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This bundle contains everything you need to know for the fifth interim exam for the course "Scientific & Statistical Reasoning" given at the University of Amsterdam. It contains both articles, book chapters and lectures. It consists of the following materials:

...

Scientific & Statistical Reasoning – Summary interim exam 3 (UNIVERSITY OF AMSTERDAM)

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This bundle contains everything you need to know for the fifth interim exam for the course "Scientific & Statistical Reasoning" given at the University of Amsterdam. It contains both articles, book chapters and lectures. It consists of the following materials:

...
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Summaries & Study Note of JesperN
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