A Gentle Introduction to Statistical Sampling and Resampling

Data is the currency of applied machine learning. Therefore, it is important that it is both collected and used effectively. Data sampling refers to statistical methods for selecting observations from the domain with the objective of estimating a population parameter. Whereas data resampling refers to…

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…

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…

How to Use Statistics to Identify Outliers in Data

When modeling, it is important to clean the data sample to ensure that the observations best represent the problem. Sometimes a dataset can contain extreme values that are outside the range of what is expected and unlike the other data. These are called outliers and…

Implementing Healthcare Interventions: Context is Key | PLOS Blogs Network

0000-0002-8715-2896 “One of the most important parts of our job is to listen to partners, adjust the strategies based on what they hear, and give implementers the leeway to use their expertise and their local VISIT THE SOURCE ARTICLE Implementing Healthcare Interventions: Context is Key…