Summary
Get a summary of the essential points that were covered in this chapter.
Let’s summarize what we have learned so far in this section.

Error distribution: We saw that data from a normal or Gaussian error distribution has a relatively even spread of values above and below the mean. This data can be made up of any real number(s), positive or negative.

Normality tests: We learned that data can be tested for normality with a normality test, such as the ShapiroWilk Test. Still, it’s often better to use a function like
fitdistr()
to estimate which predefined error distribution the data is most similar to. 
Logtransformation: We saw that logtransformation is a common technique to help normalize many biological data.

Nonparametric tests are not enough: We discussed that many old nonparametric statistical tests can analyze nonnormally distributed data, but these have fallen out of favor with the rise of generalized linear models.

Ttests: We learned that Student’s ttest is designed for analyzing the difference in means of normally distributed data in two categories. A ttest is designed for small sample sizes, and large sample sizes are equivalent to a linear model.

Linear models: We also discussed that linear models are one of the most commonly used forms of statistics available.
Linear models allow us to analyze a normally distributed response variable with any combination of categorical or continuous predictor variables.

Useful functions: We also saw many useful functions for all linear models, such as
summary()
,Anova()
from the car package, andplot()
. Posthoc tests can be accomplished using the emmeans package.
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