The Friedman test is a non-parametric alternative to repeated measures ANOVA, designed for use with ordinal data or non-normal distributions. Learn how and when to use it in your analyses.
Data science is a key driver of sustainability, offering insights that help optimize resources, reduce waste, and improve the energy efficiency of supply chains.
Real-time data processing platforms like Apache Flink are revolutionizing epidemiological surveillance by providing timely, accurate insights that enable rapid response to disease outbreaks and public health threats.
Explore Type I and Type II errors in hypothesis testing. Learn how to balance error rates, interpret significance levels, and understand the implications of statistical errors in real-world scenarios.
The ARIMAX model extends ARIMA by integrating exogenous variables into time series forecasting, offering more accurate predictions for complex systems.
A detailed look at hypothesis testing, the misconceptions around the null hypothesis, and the diverse methods for detecting data deviations.
Learn the key differences between ANOVA and Kruskal-Wallis tests, and understand when to use each method based on your data’s assumptions and characteristics.
The Cox Proportional Hazards Model is a vital tool for analyzing time-to-event data in medical studies. Learn how it works and its applications in survival analysis.
Residual diagnostics often trigger debates, especially when tests like Shapiro-Wilk suggest non-normality. But should it be the final verdict on your model? Let’s dive deeper into residual analysis, focusing on its impact in GLS, mixed models, and robust alternatives.
Most diagrams for choosing statistical tests miss the bigger picture. Here’s a bold, practical approach that emphasizes interpretation over mechanistic rules, and cuts through statistical misconceptions like the N>30 rule.