Chi-squared

observed counts -the no. of occurrences in each category observed in the sample
expected counts - the no. of occurrences in each category expected under the null hypothesis
χ2 - the test statistic represents the sum of the squared (and scaled) differences between observed and expected counts
degrees of freedom - determines the distribution’s shape and proportions
- i.e. they are the number of values that are free to vary
goodness of fit test - tests whether a sample of frequency data came from a population with a specific, known distribution
continuous data - represents some measurement or score on a scale (e.g. reaction time)
- answers question: how much?
categorical data - represents membership in a particular group or condition (e.g. control vs experimental group)
- answers question: which one?

two variables → both categorical
data (in this context) - counts how many observations fall into each combinations of categories
use Fisher’s exact test (fisher.test()) - work under the assumption that for χ2 expected frequencies are greater than 5
- statistical assumptions - a precondition that must be true for a statistical test to work as expected (if these assumptions are violated/not true, then the test may give inaccurate or misleading estimates or results)
association is not causation \n \n