In various posts, particularly those on regression analysis, variance analysis, and time series, we’ve come across terms that seem deliberately designed to scare the reader.
The aim of these articles is to explain these key concepts simply, beyond the apparent complexity (something I really wanted when I was a student, instead of facing texts written in a purposely convoluted and unnecessarily difficult way).
So, it’s time to spend a few words on three very important concepts that often recur in statistical analysis and need to be well understood. The reality is much, much clearer than it seems, so… don’t be afraid!
Analysis of Variance, ANOVA. Explained simply
Analysis of Variance (ANOVA) is a parametric test that evaluates the differences between the means of two or more data groups.
It is a statistical hypothesis test that is widely used in scientific research and allows to determine if the means of at least two populations are different.
As a minimum prerequisite, a continuous dependent variable and a categorical independent variable that divides the data into comparison groups are required.
Statistical Parametric and Non-Parametric Tests
Statistical tests can be either parametric or non-parametric.
Parametric Tests: The Power of Normality
- Parametric tests assume an approximately normal distribution.
- They involve continuous or interval-type variables and require a sufficiently large sample size (typically > 30).
- They also assume homogeneity of variances (homoscedasticity).
These tests have a higher statistical power because they provide a greater probability of correctly rejecting a false statistical hypothesis.
Continue reading “Statistical Parametric and Non-Parametric Tests”Multiple Regression Analysis, Explained Simply
The phenomena we observe and wish to study in order to deepen our understanding rarely present themselves so simply as to be defined by only two variables: one predictive (independent) and one response (dependent).
Therefore, while simple linear regression analysis holds fundamental theoretical importance, in practice it provides little more information than simply studying the correlation coefficient.
Continue reading “Multiple Regression Analysis, Explained Simply”The Data: The 4 Scales of Measurement
The 4 scales of measurement. I understand the instinctive reaction: to skip an article that tastes like an unexciting introduction to a topic considered trivial.
However, I ask readers for an effort that I think is worth making. The concepts presented in this article are basic and precisely for this reason they have fundamental value and importance.
Assimilating these concepts means building a solid foundation for the topics that will follow.
Put concisely, but resolutely: we take nothing for granted, because nothing is taken for granted.
Continue reading “The Data: The 4 Scales of Measurement”