A number of inferential statistical tests (A/B tests and significance tests) assume that the underlying that we’re comparing come from a normal (Gaussian) distribution. However, this isn’t generally true for a number of data sets in practice. In order to use the tools that assume normality, we have to transform the data (and the limits […]Read more "Johnson Transformation for Non-Normal Data"
Introduction The more advanced methods in statistics have generally been developed to answer real-world questions, and ANOVA is no different. How do we answer questions in the real world, as to which route from home to work on your daily commute is easier, or How would you know which air-conditioner to choose out of a […]Read more "Two Way ANOVA in R"
Outliers are points in a data set that lie far away from the estimated value of the centre of the data set. This estimated centre could be either the mean, or median, depending on what kind of point or interval estimate you’re using. Outliers tend to represent something different from “the usual” that you might […]Read more "Simple Outlier Detection in R"
When we see data visualized in a graph such as a histogram, we tend to draw some conclusions from it. When data is spread out, or concentrated, or observed to change with other data, we often take that to mean relationships between data sets. Statisticians, though, have to be more rigorous in the way they establish their […]Read more "Normality Tests in R"