“Gigerenzer & Marewski (2015). Surrogate science: The idol of a universal method for scientific inference.” - Article summary

Good science requires statistical tools and informed judgement about what model to construct, what hypotheses to test and what tools to use.

There is no universal method of scientific inference, but, rather, a toolbox of useful scientific methods. Besides that, the danger of Bayesian statistics is that this will become a new universal method of statistics. Lastly, statistical methods are not simply applied to a discipline, they change the discipline itself.

In natural sciences, the probabilistic revolution shaped theorizing. In social sciences, it led to scientists mechanizing scientists’ inferences. Inference revolution refers to the idea that inference from sample to population was considered the most important part of research. This revolution led to a dismissive attitude towards replication.

There are three meanings of significance:

1. Mere convention
   This means that it is convenient for researchers to use 5% as a standard level of significance.
2. Alpha level
   This means that significance refers to the long-term relative frequency of making a type-I error.
3. Exact level of significance
   This is the exact level of significance and is used in null hypothesis testing using a nil and not a null of zero difference.

There are three interpretations of probability:

1. A relative frequency
   This is a long-term relative frequency.
2. Propensity
   This is the physical design of an object (e.g. dice)
3. Reasonable degree of subjective belief
   This is the degree an individual believes in somethings.

Bayesian statistics should not be used in an automatic way, like frequentism. Objections to the use of Bayes rule are that frequency-based prior probabilities do not exist (1), that the set of hypotheses needed for the prior probability distribution is not known (2) and that researchers’ introspection does not confirm the calculation of probabilities (3).

Fishing expeditions refers to the idea that hypothesis finding is the same as hypothesis testing, characterised by using a lot of p-values in a research article.