How to Use Parametric Statistical Significance Tests in Python

Parametric statistical methods often mean those methods that assume the data samples have a Gaussian distribution. in applied machine learning, we need to compare data samples, specifically the mean of the samples. Perhaps to see if one technique performs better than another on one or…

Introduction to Nonparametric Statistical Significance Tests in Python

In applied machine learning, we often need to determine whether two data samples have the same or different distributions. We can answer this question using statistical significance tests that can quantify the likelihood that the samples have the same distribution. If the data does not…

A Gentle Introduction to Statistical Hypothesis Tests

Data must be interpreted in order to add meaning. We can interpret data by assuming a specific structure our outcome and use statistical methods to confirm or reject the assumption. The assumption is called a hypothesis and the statistical tests used for this purpose are…

A Gentle Introduction to Normality Tests in Python

An important decision point when working with a sample of data is whether to use parametric or nonparametric statistical methods. Parametric statistical methods assume that the data has a known and specific distribution, often a Gaussian distribution. If a data sample is not Gaussian, then…

A Gentle Introduction to Nonparametric Statistics

A large portion of the field of statistics and statistical methods is dedicated to data where the distribution is known. Samples of data where we already know or can easily identify the distribution of are called parametric data. Often, parametric is used to refer to…

Statistics Books for Machine Learning

Statistical methods are used at each step in an applied machine learning project. This means it is important to have a strong grasp of the fundamentals of the key findings from statistics and a working knowledge of relevant statistical methods. Unfortunately, statistics is not covered…

A Gentle Introduction to the Central Limit Theorem for Machine Learning

The central limit theorem is an often quoted, but misunderstood pillar from statistics and machine learning. It is often confused with the law of large numbers. Although the theorem may seem esoteric to beginners, it has important implications about how and why we can make…

A Gentle Introduction to the Law of Large Numbers in Machine Learning

We have an intuition that more observations is better. This is the same intuition behind the idea that if we collect more data, our sample of data will be more representative of the problem domain. There is a theorem in statistics and probability that supports…

A Gentle Introduction to Calculating Normal Summary Statistics

A sample of data is a snapshot from a broader population of all possible observations that could be taken of a domain or generated by a process. Interestingly, many observations fit a common pattern or distribution called the normal distribution, or more formally, the Gaussian…

How to Use Correlation to Understand the Relationship Between Variables

There may be complex and unknown relationships between the variables in your dataset. It is important to discover and quantify the degree to which variables in your dataset are dependent upon each other. This knowledge can help you better prepare your data to meet the…