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Related Concept Videos

Randomized Experiments

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...

Group Design

Group Design

The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...

Law of Independent Assortment

Law of Independent Assortment

While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.

Dihybrid Crosses

Dihybrid Crosses

Monohybrid Crosses

Monohybrid Crosses

Regression Toward the Mean

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...

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Related Experiment Video

Updated: Jun 20, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

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Published on: January 8, 2020

A Guide to Understanding Mendelian Randomization Studies.

Kevin Nguyen¹, Braxton D Mitchell²

¹University of Maryland, Baltimore.

Arthritis Care & Research

|July 20, 2024

Summary

This summary is machine-generated.

Mendelian randomization (MR) uses genetic variants to estimate causal effects, overcoming limitations of observational epidemiology. This review explains MR methods, assumptions, and how to evaluate study quality for reliable causal inference.

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Last Updated: Jun 20, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

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Published on: January 8, 2020

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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

Epidemiology
Genetics
Biostatistics

Background:

Observational epidemiology faces limitations in causal inference due to confounding and reverse causation.
Mendelian randomization (MR) offers a method to overcome these biases using genetic variants as instrumental variables.

Purpose of the Study:

To provide a foundational understanding of Mendelian randomization (MR) studies.
To guide readers on how to critically evaluate the quality and validity of MR research.

Main Methods:

MR employs genetic variants as proxies for exposures in a natural experiment design.
Causal effects on outcomes are estimated, minimizing biases inherent in traditional epidemiologic studies.

Main Results:

MR relies on three core assumptions for valid instrument selection.
Adherence to these assumptions is crucial for assessing the credibility of causal estimates.

Conclusions:

Understanding MR methodology and its underlying assumptions is essential for interpreting results.
This review equips readers with the knowledge to assess the quality of MR studies and their causal inferences.