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Bias (statistics)
In the field of statistics, bias is a systematic tendency in which the methods used to gather data and estimate a sample statistic present an inaccurate, skewed or distorted ('' biased'') depiction of reality. Statistical bias exists in numerous stages of the data collection and analysis process, including: the source of the data, the methods used to collect the data, the estimator chosen, and the methods used to analyze the data. Data analysts can take various measures at each stage of the process to reduce the impact of statistical bias in their work. Understanding the source of statistical bias can help to assess whether the observed results are close to actuality. Issues of statistical bias has been argued to be closely linked to issues of statistical validity. Statistical bias can have significant real world implications as data is used to inform decision making across a wide variety of processes in society. Data is used to inform lawmaking, industry regulation, corp ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistics
Statistics (from German language, German: ', "description of a State (polity), state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal". Statistics deals with every aspect of data, including the planning of data collection in terms of the design of statistical survey, surveys and experimental design, experiments. When census data (comprising every member of the target population) cannot be collected, statisticians collect data by developing specific experiment designs and survey sample (statistics), samples. Representative sampling assures that inferences and conclusions can reasonably extend from the sample ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Estimation Theory
Estimation theory is a branch of statistics that deals with estimating the values of Statistical parameter, parameters based on measured empirical data that has a random component. The parameters describe an underlying physical setting in such a way that their value affects the distribution of the measured data. An ''estimator'' attempts to approximate the unknown parameters using the measurements. In estimation theory, two approaches are generally considered: * The probabilistic approach (described in this article) assumes that the measured data is random with probability distribution dependent on the parameters of interest * The set estimation, set-membership approach assumes that the measured data vector belongs to a set which depends on the parameter vector. Examples For example, it is desired to estimate the proportion of a population of voters who will vote for a particular candidate. That proportion is the parameter sought; the estimate is based on a small random sa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Bias Of An Estimator
In statistics, the bias of an estimator (or bias function) is the difference between this estimator's expected value and the true value of the parameter being estimated. An estimator or decision rule with zero bias is called ''unbiased''. In statistics, "bias" is an property of an estimator. Bias is a distinct concept from consistency: consistent estimators converge in probability to the true value of the parameter, but may be biased or unbiased (see bias versus consistency for more). All else being equal, an unbiased estimator is preferable to a biased estimator, although in practice, biased estimators (with generally small bias) are frequently used. When a biased estimator is used, bounds of the bias are calculated. A biased estimator may be used for various reasons: because an unbiased estimator does not exist without further assumptions about a population; because an estimator is difficult to compute (as in unbiased estimation of standard deviation); because a biased esti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Hypothesis Testing
A statistical hypothesis test is a method of statistical inference used to decide whether the data provide sufficient evidence to reject a particular hypothesis. A statistical hypothesis test typically involves a calculation of a test statistic. Then a decision is made, either by comparing the test statistic to a Critical value (statistics), critical value or equivalently by evaluating a p-value, ''p''-value computed from the test statistic. Roughly 100 list of statistical tests, specialized statistical tests are in use and noteworthy. History While hypothesis testing was popularized early in the 20th century, early forms were used in the 1700s. The first use is credited to John Arbuthnot (1710), followed by Pierre-Simon Laplace (1770s), in analyzing the human sex ratio at birth; see . Choice of null hypothesis Paul Meehl has argued that the epistemological importance of the choice of null hypothesis has gone largely unacknowledged. When the null hypothesis is predicted by the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Recall Bias
In epidemiological research, recall bias is a systematic error caused by differences in the accuracy or completeness of the recollections retrieved ("recalled") by study participants regarding events or experiences from the past. It is sometimes also referred to as response bias, responder bias or reporting bias. Explanation Recall bias is a type of measurement bias, and can be a methodological issue in research involving interviews or questionnaires. In this case, it could lead to misclassification of various types of exposure. Recall bias is of particular concern in retrospective studies that use a case-control design to investigate the etiology of a disease or psychiatric condition. For example, in studies of risk factors for breast cancer Breast cancer is a cancer that develops from breast tissue. Signs of breast cancer may include a Breast lump, lump in the breast, a change in breast shape, dimpling of the skin, Milk-rejection sign, milk rejection, fluid coming ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Attrition 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 of bias 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, sometim ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Funding Bias
Funding bias, also known as sponsorship bias, funding outcome bias, funding publication bias, and funding effect, is a tendency of a scientific study to support the interests of the study's financial sponsor. This phenomenon is recognized sufficiently that researchers undertake studies to examine bias in past published studies. Funding bias has been associated, in particular, with research into chemical toxicity, tobacco, and pharmaceutical drugs. It is an instance of experimenter's bias. Causes Human nature The psychology text '' Influence: Science and Practice'' describes the act of reciprocity as a trait in which a person feels obliged to return favors. This trait is embodied in all human cultures. Human nature may influence even the most ethical researchers to be affected by their sponsors, although they may genuinely deny it. Misconduct Scientific malpractice involving shoddy research or data manipulation does occur in rare instances. Often, however, the quality of manufa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Anthropic Bias (book)
''Anthropic Bias: Observation Selection Effects in Science and Philosophy'' (2002) is a book by philosopher Nick Bostrom. Bostrom investigates how to reason when one suspects that evidence is biased by "observation selection effects", in other words, when the evidence presented has been pre-filtered by the condition that there was some appropriately positioned observer to "receive" the evidence. This conundrum is sometimes called the "anthropic principle", "self-locating belief", or "indexical information". The book first discusses the fine-tuned universe hypothesis and its possible explanations, notably considering the possibility of a multiverse. Bostrom argues against the self-indication assumption (SIA), a term he uses to characterize some existing views, and introduces the self-sampling assumption (SSA). He later refines SSA into the strong self-sampling assumption (SSSA), which uses observer-moments instead of observers to address certain paradoxes in anthropic reasoning. S ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sensitivity And Specificity
In medicine and statistics, sensitivity and specificity mathematically describe the accuracy of a test that reports the presence or absence of a medical condition. If individuals who have the condition are considered "positive" and those who do not are considered "negative", then sensitivity is a measure of how well a test can identify true positives and specificity is a measure of how well a test can identify true negatives: * Sensitivity (true positive rate) is the probability of a positive test result, conditioned on the individual truly being positive. * Specificity (true negative rate) is the probability of a negative test result, conditioned on the individual truly being negative. If the true status of the condition cannot be known, sensitivity and specificity can be defined relative to a " gold standard test" which is assumed correct. For all testing, both diagnoses and screening, there is usually a trade-off between sensitivity and specificity, such that higher sensiti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Spectrum Bias
In biostatistics, spectrum bias refers to the phenomenon that the performance of a diagnostic test may vary in different clinical settings because each setting has a different mix of patients. Because the performance may be dependent on the mix of patients, performance at one clinic may not be predictive of performance at another clinic. These differences are interpreted as a kind of ''bias''. Mathematically, the spectrum bias is a sampling bias and not a traditional statistical bias; this has led some authors to refer to the phenomenon as ''spectrum effects'', whilst others maintain it is a bias if the true performance of the test differs from that which is 'expected'. Usually the performance of a diagnostic test is measured in terms of its sensitivity and specificity and it is changes in these that are considered when referring to spectrum bias. However, other performance measures such as the likelihood ratios may also be affected by spectrum bias. Generally spectrum bias is co ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lippincott Williams & Wilkins
Lippincott Williams & Wilkins (LWW) is an American imprint (trade name), imprint of the American Dutch publishing conglomerate Wolters Kluwer. It was established by the acquisition of Williams & Wilkins and its merger with J.B. Lippincott Company in 1998. Under the LWW brand, Wolters Kluwer, through its Health Division, publishes scientific, technical, and medical content such as textbooks, reference works, and over 275 scientific journals (most of which are medical or other public health journals). Publications are aimed at physicians, nurses, clinicians, and students. Overview LWW grew out of the gradual consolidation of various earlier independent publishers by Wolters Kluwer. Predecessor Wolters Samson acquired Raven Press of New York in 1986. Wolters Samson merged with Kluwer in 1987. The merged company bought J. B. Lippincott & Co. of Philadelphia in 1990; it merged Lippincott with the Raven Press to form Lippincott-Raven in 1995. In 1997 and 1998, Wolters Kluwer acquired Tho ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Berkson's Paradox
Berkson's paradox, also known as Berkson's bias, collider bias, or Berkson's fallacy, is a result in conditional probability and statistics which is often found to be counterintuitive, and hence a veridical paradox. It is a complicating factor arising in statistical tests of proportions. Specifically, it arises when there is an ascertainment bias inherent in a study design. The effect is related to the explaining away phenomenon in Bayesian networks, and Collider (statistics), conditioning on a collider in graphical models. This paradox is often illustrated using scenarios from the fields of medical statistics or biostatistics, as in the original description of the problem by Joseph Berkson. Examples Overview The most common example of Berkson's paradox is a false observation of a ''negative'' correlation between two desirable traits, i.e., that members of a population which have some desirable traits tend to lack a second. Berkson's paradox occurs when this observation appe ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
