In the last blog post I created a simple simulation of statistical power (probability to identify effects when they are really there) calulation depending on the sample size and effect size (Cohen’s D using Will Hopkins effect levels).
Now I will continue with the simulations and create 10 ES (from 0xSD [Baseline group] to 1xSD) levels with sample sizes going from 5 to 100 in 5 increment. We are going to resample 2000x
Understanding Central Limit Theorem is of EXTREME importance in statistics. This concept usually sets the boundary line between people who understand statistics and people who don’t.
Latest obsession of the researches is individual variation in training responses. The motivation behind this approach (known and emphasized in theory of training as individualization principle) is the creation of personalized medicine or personalized training.
Over the last month I have been learning R programming language that is completely FREE and used extensively in statistics and data analysis. I have been also playing with ggplot2 which is a great graphics package for R, along with knitr which was used to produce this blog post/report.
Confidence intervals gives us the range of a given statistic when generalizing from a sample to a population. The simplest example could be mean of a sample (e.g. average height) – what we are interested in are the generalizations (or inferences) from this sample to a population (e.g. average height in population). Due the sampling error we are not…
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