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.
To learn more about Bayesian
vs. Frequentist refer to:
Frequentism vs. Bayesianism:
Jake VanderPlas- video
https://www.youtube.com/watch?v=KhAUfqhLakw
All About The Bayes: Kristin
Lennox- video
https://www.youtube.com/watch?v=eDMGDhyDxuY&t=3041s
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
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