Bias (+ Confounding?)

1. Information bias: it can occur during data collection

1.1 misclassification bias: for example, in a case-control study, some people who have the disease may be misclassified as controls, and some without the disease may be misclassified as cases.

1.2 Recall bias: for example, women who had a baby with a malformation tended to remember more mild infections that occurred during their pregnancies than did mothers of normal infants.

2. selection bias: who participant in the study differ from who would have been eligible to participant but were unwilling or not selected.

2.1 incidence-prevalence bias: patients die too fast that could be ignored in the study

2.2 participation bias: who participants in the study differ from who is eligible but not participant

1) Spectrum bias - when the population under investigation does not reflect the general population or the clinically relevant population.

2) Reporting bias - defined as "selective revealing or suppression of information" by subjects. For instance, some subjects do not fully report information about past medical history, or smoking. Sometimes reporting bias is also caused by tendency of the researchers to under-report unexpected or undesirable experimental results.

3) Information bias (misclassification bias) - due to inaccurate measurement or classification of disease, exposure or other variables. For example, an inaccurately calibrated instrument or the situation when some individuals consistently have missing data

1. Belief bias - An effect where someone's evaluation of the logical strength of an argument is biased by the believability of the conclusion.

2. Distinction bias - The tendency to view two options as more dissimilar when evaluating them simultaneously than when evaluating them separately.

3. Data-snooping bias - misusing data mining techniques to uncover relationships in data

1. Cognitive bias: a pattern of deviation in judgement, whereby inferences about other people and situations may be drawn in an illogical fashion. 2. Omitted-variable bias: is created when the model compensates for the missing factor by over- or underestimating the effect of one of the other factors. 3. Systematic errors: a measurement which lead to the situation where the mean of many separate measurements differs significantly from the actual value of the measured attribute

1. Experimentar Bias：occurs when the measurements obtained in a study are influenced by the experimenter's expectations regarding the outcome of the study. 2. Recency Bias: cause people to more prominently recall and emphasize recent events and observations than those that occurred in the near or distant past. 3. Confounding bias: occur when two factors are associated and the effect of one is confused with or distorted by the effect of the other.

1) Monte Carlo Bias: The difference between the true value of a parameter of interest and the value given by a Monte Carlo estimate using a finite monte carlo sample.

2) Survivorship Bias: Often, the subjects that survive a particular event are not randomly selected out of a population. If one tried to make conclusions regarding an entire population using only those that the survived the event, the conclusions would be incorrect (survivorship bias would be present). A good example is the Abraham Wald WWII Planes anecdote.

3) Leading Question Bias: Non-neutral wording of a question can influence a participant's response compared to a neutral version of the question. This influence is known as a leading question bias. For example: "Did you enjoy the critically acclaimed Academy Award-winning film entitled Twelve Years a Slave" is a leading question compared to "Did you enjoy the film entitled Twelve Years a Slave". Mentioning the acclaim a film has been given could affect the participant's response.

1. In the study of machine learning, the inductive bias is the set of assumptions needed by the learner to predict outputs from inputs not already received. eg. max margin in svm or Occam's razor

2. In cognitive science, memory bias is a bias which either increases or decreases the recall of a memory. For example, the Google effect claims that people are less likely to remember facts which can be easily looked up via Google.

3. Closer to statistical science, funding bias is the possibility that outcomes or test procedures may be selected that favor a study's sponsor

1) Attrition Bias: Caused by loss of participants. People who drop out of a trial might just be ignored by investigation, but the drop out could be experiment related. For example in cancer investigation someone might drop out to get treatment abroad due to drugs not working.

2) Publication Bias: (Bit different) Reports are far more likely to be published if they contain significant results rather than null hypothesis result. Often leads to unreported results which could effect other experiments.

3) Performance Bias: Systematic differences in the care provided to the participants in the comparison groups other than the intervention under investigation.