## Wednesday, January 8, 2020

### Research Statistics: Frequentist & Bayesian

Learning about stats will help you think in terms of probabilities, and allow you to gain a better understanding of research data. Discussions on research stats generally involve two categories: Frequentist and Bayesian. Frequentist methods refers to quantities that are hypothetical frequencies of data distribution patterns under an assumed statistical model. These hypothetical frequencies that are predicted are called frequency probabilities. These probabilities are not synonymous with hypothesis probabilities. Bayesian statistics are also concerned with probability and present mathematical models of data. The formula use is different with Bayesian vs. Frequentist models; a key difference is how probability is conceptualized. My knowledge is in Frequentist stats; I don't have the knowledge to talk about Bayesian models, so discussions in this book, regarding stats, will be focused on Frequentist stats.

Frequentism vs. Bayesianism: Jake VanderPlas- video

All About The Bayes: Kristin Lennox- video

Statistical Modeling, Causal Inference and Social Science
https://statmodeling.stat.columbia.edu/

Myths about statistics https://www.statisticsdonewrong.com/

Most scientific and technical journals contain some form of statistics; that is, if the research is quantitative. Without an understanding of statistics, the statistical information contained in the journal will be meaningless. An understanding of basic statistics will provide you with the fundamental skills necessary to read and evaluate most results sections. The ability to extract meaning from journal articles, and the ability to evaluate research from a statistical perspective are basic skills that will increase your knowledge and understanding of the article of interest.
Gaining knowledge in the area of statistics will help you become a better-informed consumer. If you understand basic statistical concepts, you will be in a better position to evaluate the information you have been given.

People like assertions that reflect certainty. Statistical, scientific thinking is not about absolute certainty. The conclusions drawn from scientific research are probabilistic- generalizations that are correct most of the time, but not every time. People often weight anecdotal evidence more heavily than probabilistic information. This is an error in thinking, leads to bad decisions, and often, irrational thinking. It is important to accept statistical predictions aren't perfect. These predictions are based on samples (groups, categories intending to represent populations) and will be correct more often than not.

To learn more about statistical thinking refer to - In Evidence We Trust