Catalogue of Bias Collaboration, Nunan D, Bankhead C, Aronson JK. Selection bias. Catalogue Of Bias 2017: [www.catalogofbias.org/biases/selection-bias/](https://www.catalogofbias.org/biases/selection-bias/) 

Catalog of Bias

Recall bias is a error that occurs when participants do not remember previous events or experiences accurately or omit details: the accuracy and volume of memories may be influenced by subsequent events and experiences.

Recall Bias

When a single node is caused by two other nodes we encounter something called "collider-bias" where one condition explains-away another. 

Since A and C both effect the outcome of B, by conditioning on B, A and C become **dependent**.

$A \rightarrow B \leftarrow C$: The Collider

This type of bias is also known as "Confounded." It happens when good controls or confounders are not included in the regression model.

Omitted variable bias

Sampling bias is a systematic error that occurs when the method used to select participants results in a sample that fails to accurately represent the broader target population. Because the selected individuals differ in important ways from the overall group, any statistical findings derived from this unrepresentative sample will likely be inaccurate and cannot be safely generalized.

Sampling Bias

- Recall bias
- Selection bias
- Collider bias
- Omitted variable bias

University of Michigan - Ann Arbor

Bias is a systematic error in the estimate of an association between cause and effect. It may result from poor diagnosis/diagnostic criteria, poor case choice, poor choice of controls or variation in the way risk exposure is measured in case and controls.

Bias (Scientific)

Pearl, J., & Mackenzie, D. (2018). $\mathit{The \ Book \ of \ Why: \ The \ New \ Science \ of \ Cause \ and \ Effect.}$ Basic Books.

The Book of Why

Types of Bias

http://approximatelycorrect.com/2016/11/07/the-foundations-of-algorithmic-bias/http://approximatelycorrect.com/2016/11/07/the-foundations-of-algorithmic-bias/

The Foundations of Algorithmic Bias



This article talks about how algorithms make decisions, how does machine learning relate to it, and it dissects many foundation ways algorithm and ML can go wrong and cause biases. It is a good indepth article on this topic.

algorithm and bias

$\mathrm{bias}(\hat \theta_m) = \mathbb{E}(\hat \theta_m) - \theta$, 

where $\hat{\theta}_m$ is the the estimator of a paramter, $\theta$ is the true value of that parameter, and $m$ is the sample size/number of data points. 

The bias of an estimator for a parameter is the difference between the expected value that the estimator will take when trained on data and the actual value of the parameter used to generate the data.

Mathematical Defintion of Bias

A point estimator is a function that returns an estimate denoted by $\hat\theta$, using a given set of data as an input 

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