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Selection Bias
Selection bias is the bias introduced by the selection of individuals, groups, or data for analysis in such a way that proper randomization is not achieved, thereby failing to ensure that the sample obtained is representative of the population intended to be analyzed. It is sometimes referred to as the selection effect. The phrase "selection bias" most often refers to the distortion of a statistical analysis, resulting from the method of collecting samples. If the selection bias is not taken into account, then some conclusions of the study may be false. Types Sampling bias Sampling bias is systematic error due to a non-random sample of a population, causing some members of the population to be less likely to be included than others, resulting in a biased sample, defined as a statistical sample of a population (or non-human factors) in which all participants are not equally balanced or objectively represented. It is mostly classified as a subtype of selection bias, sometimes sp ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bias
Bias is a disproportionate weight ''in favor of'' or ''against'' an idea or thing, usually in a way that is closed-minded, prejudicial, or unfair. Biases can be innate or learned. People may develop biases for or against an individual, a group, or a belief. In science and engineering, a bias is a systematic error. Statistical bias results from an unfair sampling of a population, or from an estimation process that does not give accurate results on average. Etymology The word appears to derive from Old Provençal into Old French ''biais'', "sideways, askance, against the grain". Whence comes French ''biais'', "a slant, a slope, an oblique". It seems to have entered English via the game of bowls, where it referred to balls made with a greater weight on one side. Which expanded to the figurative use, "a one-sided tendency of the mind", and, at first especially in law, "undue propensity or prejudice". Types of bias Cognitive biases A cognitive bias is a repeating or basic mi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lagging (epidemiology)
In epidemiology, lagging (or ''exposure lagging'') means excluding the exposure in a time period before registration of an outcome. It may be motivated by that the actual outcome had actually occurred before the registration of it, and that the last exposure before registration did not contribute to the case. For example, when studying risk factors of cancer, the cancer process may have been triggered long before actual diagnosis of cancer, and that therefore any exposure to risk factors in the ''lag'' time between may be unimportant. It can be used to mitigate protopathic bias Selection bias is the bias introduced by the selection of individuals, groups, or data for analysis in such a way that proper randomization is not achieved, thereby failing to ensure that the sample obtained is representative of the population int ..., that is, when a treatment for the first symptoms of a disease or other outcome appear to cause the outcome. Protopathic bias is a potential bias when there ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lost To Follow-up
In the clinical research trial industry, loss to follow-up refers to patients who at one point in time were actively participating in a clinical research trial, but have become lost (either by error in a computer tracking system or by being unreachable) at the point of follow-up in the trial. These patients can become lost for many reasons. Without properly informing the investigator associated with the clinical trial, they may have opted to withdraw from the clinical trial, moved away from the particular study site during the clinical trial, become ill and unable to communicate, are missing or are deceased. Adverse effects Patients who become lost to follow-up during a clinical research trial result in many negative effects on the outcome of the trial and on the pharmaceutical company sponsoring the clinical research trial. Patients who are lost-to-follow-up lead to incomplete study results, which in turn can put a bias on the result of the study as well as a bias on the inve ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Health Intervention
A public health intervention is any effort or policy that attempts to improve mental and physical health on a population level. Public health interventions may be run by a variety of organizations, including governmental health departments and non-governmental organizations (NGOs). Common types of interventions include screening programs, vaccination, food and water supplementation, and health promotion. Common issues that are the subject of public health interventions include obesity, drug, tobacco, and alcohol use, and the spread of infectious disease, e.g. HIV. A policy may meet the criteria of a public health intervention if it prevents disease on both the individual and community level and has a positive impact on public health. Types Health interventions may be run by a variety of organizations, including health departments and private organizations. Such interventions can operate at various scales, such as on a global, country, or community level. The whole populat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Response Rate (survey)
In survey research, response rate, also known as completion rate or return rate, is the number of people who answered the survey divided by the number of people in the sample. It is usually expressed in the form of a percentage. The term is also used in direct marketing to refer to the number of people who responded to an offer. The general consensus in academic surveys is to choose one of thsix definitions summarized by the American Association for Public Opinion Research(AAPOR). These definitions are endorsed by the National Research Council and the Journal of the American Medical Association, among other well recognized institutions. They are: # Response Rate 1 (RR1) – or the minimum response rate, is the number of complete interviews divided by the number of interviews (complete plus partial) plus the number of non-interviews (refusal and break-off plus non-contacts plus others) plus all cases of unknown eligibility (unknown if housing unit, plus unknown, other). # Response ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Failure Bias
Failure bias is the logical error of concentrating on the people or things that failed to make it past some selection process and overlooking those that did, typically because of their lack of visibility. This can lead to false conclusions in several different ways. It is a form of selection bias. In several of these cases, one measure of success is precisely the lack of public awareness of people or things that are undergoing such selection process, which means that, in the possibility that there is at least one agent who is interested in the success of the people or things that are going through the selection process, the agent(s) will be interested into keeping the subject out of the public eye, and thus, to raise the likelihood of the failure bias happening Examples * Espionage * Most crimes fit into this category * Censorship See also * Selection bias * Cherry picking * Econometrics * ''Fooled by Randomness'' * Meta-analysis * Multiple comparisons problem * Selection prin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Survivorship Bias
Survivorship bias or survival bias is the logical error of concentrating on entities that passed a selection process while overlooking those that did not. This can lead to incorrect conclusions because of incomplete data. Survivorship bias is a form of selection bias that can lead to overly optimistic beliefs because multiple failures are overlooked, such as when companies that no longer exist are excluded from analyses of financial performance. It can also lead to the false belief that the successes in a group have some special property, rather than just coincidence as in correlation "proves" causality. Another kind of survivorship bias would involve thinking that an incident was not all that dangerous because the only people who were involved in the incident who can speak about it are those who survived it. Even if one knew that some people are dead, they would not have their voice to add to the conversation, leading to bias in the conversation. As a general experimental ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Dredging
Data dredging (also known as data snooping or ''p''-hacking) is the misuse of data analysis to find patterns in data that can be presented as statistically significant, thus dramatically increasing and understating the risk of false positives. This is done by performing many statistical tests on the data and only reporting those that come back with significant results. The process of data dredging involves testing multiple hypotheses using a single data set by exhaustively searching—perhaps for combinations of variables that might show a correlation, and perhaps for groups of cases or observations that show differences in their mean or in their breakdown by some other variable. Conventional tests of statistical significance are based on the probability that a particular result would arise if chance alone were at work, and necessarily accept some risk of mistaken conclusions of a certain type (mistaken rejections of the null hypothesis). This level of risk is called the ''s ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Combinatorial Meta-analysis
Combinatorial meta-analysis (CMA) is the study of the behaviour of statistical properties of combinations of studies from a meta-analytic dataset (typically in social science research). In an article that develops the notion of "gravity" in the context of meta-analysis, Dr. Travis GeeGee, T. (2005) "Capturing study influence: The concept of 'gravity' in meta-analysis", ''Counselling, Psychotherapy, and Health'', 1(1), 52–7 proposed that the jackknife methods applied to meta-analysis in that article could be extended to examine all possible combinations of studies (where practical) or random subsets of studies (where the combinatorics of the situation made it computationally infeasible). Concept In the original article, ''k'' objects (studies) are combined ''k''-1 at a time ( jackknife estimation), resulting in ''k'' estimates. It is observed that this is a special case of the more general approach of CMA which computes results for ''k'' studies taken 1, 2, 3 ... ''k'' &m ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Meta-analysis
A meta-analysis is a statistical analysis that combines the results of multiple scientific studies. Meta-analyses can be performed when there are multiple scientific studies addressing the same question, with each individual study reporting measurements that are expected to have some degree of error. The aim then is to use approaches from statistics to derive a pooled estimate closest to the unknown common truth based on how this error is perceived. Meta-analytic results are considered the most trustworthy source of evidence by the evidence-based medicine literature.Herrera Ortiz AF., Cadavid Camacho E, Cubillos Rojas J, Cadavid Camacho T, Zoe Guevara S, Tatiana Rincón Cuenca N, Vásquez Perdomo A, Del Castillo Herazo V, & Giraldo Malo R. A Practical Guide to Perform a Systematic Literature Review and Meta-analysis. Principles and Practice of Clinical Research. 2022;7(4):47–57. https://doi.org/10.21801/ppcrj.2021.74.6 Not only can meta-analyses provide an estimate of the un ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Outlier
In statistics, an outlier is a data point that differs significantly from other observations. An outlier may be due to a variability in the measurement, an indication of novel data, or it may be the result of experimental error; the latter are sometimes excluded from the data set. An outlier can be an indication of exciting possibility, but can also cause serious problems in statistical analyses. Outliers can occur by chance in any distribution, but they can indicate novel behaviour or structures in the data-set, measurement error, or that the population has a heavy-tailed distribution. In the case of measurement error, one wishes to discard them or use statistics that are robust to outliers, while in the case of heavy-tailed distributions, they indicate that the distribution has high skewness and that one should be very cautious in using tools or intuitions that assume a normal distribution. A frequent cause of outliers is a mixture of two distributions, which may be two dist ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Availability Heuristic
The availability heuristic, also known as availability bias, is a mental shortcut that relies on immediate examples that come to a given person's mind when evaluating a specific topic, concept, method, or decision. This heuristic, operating on the notion that, if something can be recalled, it must be important, or at least more important than alternative solutions not as readily recalled, is inherently biased toward recently acquired information. The mental availability of an action's consequences is positively related to those consequences' perceived magnitude. In other words, the easier it is to recall the consequences of something, the greater those consequences are often perceived to be. Most notably, people often rely on the content of their recall if its implications are not called into question by the difficulty they have in recalling it. Overview and history In the late 1960s and early 1970s, Amos Tversky and Daniel Kahneman began work on a series of papers examining "heur ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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