Addressing implicit requires deliberate efforts to promote diversity, equity, and inclusion in research practices. Researchers can engage in cultural competency training, collaborate with diverse…

What some the statistician-approved ways of handling missing data? Biostatisticians have developed various useful techniques to deal with these holes in your dataset. The infographic below outlines 5…

If you heavily rely on conventional (frequentist) statistical tests like t-tests or ANOVAs in your research, it’s important to understand the principles behind the null hypothesis and NHST. Keeping…

Interested in getting started with Bayesian statistics? Carefully selecting a prior distribution helps maintain objectivity and transparency in your Bayesian analysis. Inappropriate or poorly chosen…

Confirmation bias makes it difficult for a researcher to remain objective during the research process. However, by understanding its mechanisms and implementing strategies to mitigate its effects,…

Hidden Markov Models (HMMs) are extensively used in biomedical research for analyzing various types of sequential data, including DNA sequences, protein sequences, and biological pathways. Here are…

Sampling methods play a significant role in ensuring the representativeness and reliability of our findings. Two main approaches in sampling are non-probability and probability sampling. Let's delve…

This blog post delves into what sensitivity analysis entails, its applications, advantages, and potential limitations in the context of biomedical research.

Here we discuss how by carefully considering the various sampling methods and their respective advantages and disadvantages, you can enhance the validity and reliability of your findings. Each method…

As vast amounts of data are published each year, scientists are constantly seeking innovative methods to extract meaningful insights from such data. One method that has gained increasing attention is…