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Bias

Bias

Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...

Bias in Epidemiological Studies

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:

Probability Laws

Probability Laws

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...

Testing a Claim about Population Proportion

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...

Determination of Expected Frequency

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...

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Related Experiment Video

Updated: Mar 31, 2026

Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions

Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions

Published on: June 21, 2019

Bounding formulas for selection bias.

Tzu-Hsuan Huang, Wen-Chung Lee

American Journal of Epidemiology

|November 1, 2015

Summary

This summary is machine-generated.

Researchers can now better interpret observational study results using new bounding formulas for selection bias and unmeasured confounding. These methods address limitations in existing statistical tools for bias assessment.

Keywords:

confounding effect modification epidemiologic methods selection bias

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Area of Science:

Epidemiology
Biostatistics

Background:

Observational studies are susceptible to selection bias, information bias, and confounding.
Statistical methods effectively address information and confounding biases.
Methods for selection bias and unmeasured confounding remain underdeveloped.

Purpose of the Study:

To propose general bounding formulas for bias in observational studies.
To specifically address selection bias and unmeasured confounding.
To aid researchers in interpreting potentially biased results.

Main Methods:

Development of general bounding formulas for bias.
Application of formulas to selection bias and unmeasured confounding.
Theoretical framework for bias quantification.

Main Results:

Novel bounding formulas for quantifying selection bias and unmeasured confounding.
Provides a quantitative approach to assess bias.
Enhances understanding of bias impact in observational research.

Conclusions:

The proposed formulas offer a valuable tool for researchers.
Facilitates more cautious and informed interpretation of study findings.
Advances methods for addressing bias in observational epidemiology.