What O We Mean By Biased Results Or Systematic Errors In Impact And Evaluation PdfBy Rhelixclamit1955 In and pdf 20.01.2021 at 11:01 8 min read
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- Sampling bias
- Algorithmic bias detection and mitigation: Best practices and policies to reduce consumer harms
- Biases and Confounding
Quantitative bias analysis serves several objectives in epidemiological research. First, it provides a quantitative estimate of the direction, magnitude and uncertainty arising from systematic errors. Second, the acts of identifying sources of systematic error, writing down models to quantify them, assigning values to the bias parameters and interpreting the results combat the human tendency towards overconfidence in research results, syntheses and critiques and the inferences that rest upon them.
Observational error or measurement error is the difference between a measured value of a quantity and its true value. Variability is an inherent part of the results of measurements and of the measurement process. Measurement errors can be divided into two components: random error and systematic error. Random errors are errors in measurement that lead to measurable values being inconsistent when repeated measurements of a constant attribute or quantity are taken. Systematic errors are errors that are not determined by chance but are introduced by an inaccuracy involving either the observation or measurement process inherent to the system. When either randomness or uncertainty modeled by probability theory is attributed to such errors, they are "errors" in the sense in which that term is used in statistics ; see errors and residuals in statistics. Every time we repeat a measurement with a sensitive instrument, we obtain slightly different results.
Algorithmic bias detection and mitigation: Best practices and policies to reduce consumer harms
In statistics , sampling bias is a bias in which a sample is collected in such a way that some members of the intended population have a lower or higher sampling probability than others. It results in a biased sample , a non-random sample  of a population or non-human factors in which all individuals, or instances, were not equally likely to have been selected. Medical sources sometimes refer to sampling bias as ascertainment bias. Sampling bias is usually classified as a subtype of selection bias ,  sometimes specifically termed sample selection bias ,    but some classify it as a separate type of bias. In this sense, errors occurring in the process of gathering the sample or cohort cause sampling bias, while errors in any process thereafter cause selection bias.
Before concluding that an individual study's conclusions are valid, one must consider three sources of error that might provide an alternative explanation for the findings. These are:. If a determination is made that the findings of a study were not due to any one of these three sources of error, then the study is considered internally valid. In other words, the conclusions reached are likely to be correct for the circumstances of that particular study. This does not not necessarily mean that the findings can be generalized to other circumstances external validity. For example, the Physicians' Health Study concluded that aspirin use reduced the risk of myocardial infarction in adult male physicians in the United States. The study was carefully done, and the study was internally valid, but it was not clear that the results could be extrapolated to women, or even to non-physicians whose risk of myocardial infarction is generally lower than that of the population overall.
The private and public sectors are increasingly turning to artificial intelligence AI systems and machine learning algorithms to automate simple and complex decision-making processes. AI is also having an impact on democracy and governance as computerized systems are being deployed to improve accuracy and drive objectivity in government functions. The availability of massive data sets has made it easy to derive new insights through computers. As a result, algorithms, which are a set of step-by-step instructions that computers follow to perform a task, have become more sophisticated and pervasive tools for automated decision-making. In the pre-algorithm world, humans and organizations made decisions in hiring, advertising, criminal sentencing, and lending. These decisions were often governed by federal, state, and local laws that regulated the decision-making processes in terms of fairness, transparency, and equity. Today, some of these decisions are entirely made or influenced by machines whose scale and statistical rigor promise unprecedented efficiencies.
Biases and Confounding
A cognitive bias is a systematic error in thinking that occurs when people are processing and interpreting information in the world around them and affects the decisions and judgments that they make. Cognitive biases are often a result of your brain's attempt to simplify information processing. Biases often work as rules of thumb that help you make sense of the world and reach decisions with relative speed. Because of this, subtle biases can creep in and influence the way you see and think about the world. The concept of cognitive bias was first introduced by researchers Amos Tversky and Daniel Kahneman in
Medwave se preocupa por su privacidad y la seguridad de sus datos personales. Biomedical research, particularly when it involves human beings, is always subjected to sources of error that must be recognized. Systematic error or bias is associated with problems in the methodological design or during the execu-tion phase of a research project. It affects its validity and is qualitatively ap-praised. On the other hand, random error is related to variations due to chance.