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P-hacking
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 ''si ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Spurious Correlations - Spelling Bee Spiders
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Spurious may refer to: * Spurious relationship in statistics * Spurious emission or spurious tone in radio engineering * Spurious key in cryptography * Spurious interrupt in computing * Spurious wakeup in computing * ''Spurious'', a 2011 novel by Lars Iyer Lars Iyer is a British novelist and philosopher of Indian/Danish parentage. He is best known for a trilogy of short novels: ''Spurious'' (2011), ''Dogma'' (2012), and ''Exodus'' (2013), all published by Melville House. Iyer has been shortlisted f ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Abacavir
Abacavir, sold under the brand name Ziagen among others, is a medication used to treat HIV/AIDS. Similar to other nucleoside analog reverse-transcriptase inhibitors (NRTIs), abacavir is used together with other HIV medications, and is not recommended by itself. It is taken by mouth as a tablet or solution and may be used in children over the age of three months. Abacavir is generally well tolerated. Common side effects include vomiting, insomnia (trouble sleeping), fever, and feeling tired. Other common side effects include loss of appetite, headache, nausea (feeling sick), diarrhea, rash, and lethargy (lack of energy). More severe side effects include hypersensitivity, liver damage, and lactic acidosis. Genetic testing can indicate whether a person is at higher risk of developing hypersensitivity. Symptoms of hypersensitivity include rash, vomiting, and shortness of breath. Abacavir is in the NRTI class of medications, which work by blocking reverse transcriptase, an enzy ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Demographic Data
Demography () is the statistical study of populations, especially human beings. Demographic analysis examines and measures the dimensions and dynamics of populations; it can cover whole societies or groups defined by criteria such as education, nationality, religion, and ethnicity. Educational institutions usually treat demography as a field of sociology, though there are a number of independent demography departments. These methods have primarily been developed to study human populations, but are extended to a variety of areas where researchers want to know how populations of social actors can change across time through processes of birth, death, and migration. In the context of human biological populations, demographic analysis uses administrative records to develop an independent estimate of the population. Demographic analysis estimates are often considered a reliable standard for judging the accuracy of the census information gathered at any time. In the labor ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Cancer Cluster
A cancer cluster is a disease cluster in which a high number of cancer cases occurs in a group of people in a particular geographic area over a limited period of time.Cancer Cluster FAQ Centers for Disease Control and Prevention, National Center for Environmental Health, Division of Environmental Hazards and Health Effects. Historical examples of work-related cancer clusters are well documented in the medical literature. Notable examples include: scrotal cancer among s in 18th century ; |
Predictive Modelling
Predictive modelling uses statistics to predict outcomes. Most often the event one wants to predict is in the future, but predictive modelling can be applied to any type of unknown event, regardless of when it occurred. For example, predictive models are often used to detect crimes and identify suspects, after the crime has taken place. In many cases the model is chosen on the basis of detection theory to try to guess the probability of an outcome given a set amount of input data, for example given an email determining how likely that it is spam. Models can use one or more classifiers in trying to determine the probability of a set of data belonging to another set. For example, a model might be used to determine whether an email is spam or "ham" (non-spam). Depending on definitional boundaries, predictive modelling is synonymous with, or largely overlapping with, the field of machine learning, as it is more commonly referred to in academic or research and development contexts. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Predictive Power
The concept of predictive power, the power of a scientific theory to generate testable predictions, differs from '' explanatory power'' and ''descriptive power'' (where phenomena that are already known are retrospectively explained or described by a given theory) in that it allows a prospective test of theoretical understanding. Examples A classic example of the predictive power of a theory is the discovery of Neptune as a result of predictions made by mathematicians John Couch Adams and Urbain Le Verrier, based on Newton's theory of gravity. Another example of the predictive power of theories or models is Dmitri Mendeleev's use of his periodic table to predict previously undiscovered chemical elements and their properties. Though largely correct, he misjudged the relative atomic masses of tellurium and iodine. Moreover, Charles Darwin used his knowledge of evolution by natural selection to predict that since a plant ('' Angraecum sesquipedale'') with a long spur in i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Meteorology
Meteorology is a branch of the atmospheric sciences (which include atmospheric chemistry and physics) with a major focus on weather forecasting. The study of meteorology dates back millennia, though significant progress in meteorology did not begin until the 18th century. The 19th century saw modest progress in the field after weather observation networks were formed across broad regions. Prior attempts at prediction of weather depended on historical data. It was not until after the elucidation of the laws of physics, and more particularly in the latter half of the 20th century the development of the computer (allowing for the automated solution of a great many modelling equations) that significant breakthroughs in weather forecasting were achieved. An important branch of weather forecasting is marine weather forecasting as it relates to maritime and coastal safety, in which weather effects also include atmospheric interactions with large bodies of water. Meteorological phen ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mean Square Error
In statistics, the mean squared error (MSE) or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors—that is, the average squared difference between the estimated values and the actual value. MSE is a risk function, corresponding to the expected value of the squared error loss. The fact that MSE is almost always strictly positive (and not zero) is because of randomness or because the estimator does not account for information that could produce a more accurate estimate. In machine learning, specifically empirical risk minimization, MSE may refer to the ''empirical'' risk (the average loss on an observed data set), as an estimate of the true MSE (the true risk: the average loss on the actual population distribution). The MSE is a measure of the quality of an estimator. As it is derived from the square of Euclidean distance, it is always a positive value that decreases as the error ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Stepwise Regression
In statistics, stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic procedure. In each step, a variable is considered for addition to or subtraction from the set of explanatory variables based on some prespecified criterion. Usually, this takes the form of a forward, backward, or combined sequence of ''F''-tests or ''t''-tests. The frequent practice of fitting the final selected model followed by reporting estimates and confidence intervals without adjusting them to take the model building process into account has led to calls to stop using stepwise model building altogetherFlom, P. L. and Cassell, D. L. (2007) "Stopping stepwise: Why stepwise and similar selection methods are bad, and what you should use," NESUG 2007. or to at least make sure model uncertainty is correctly reflected.Chatfield, C. (1995) "Model uncertainty, data mining and statistical inference," J. R. Statist. Soc. A 158, Part 3, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Covariate
Dependent and independent variables are variables in mathematical modeling, statistical modeling and experimental sciences. Dependent variables receive this name because, in an experiment, their values are studied under the supposition or demand that they depend, by some law or rule (e.g., by a mathematical function), on the values of other variables. Independent variables, in turn, are not seen as depending on any other variable in the scope of the experiment in question. In this sense, some common independent variables are time, space, density, mass, fluid flow rate, and previous values of some observed value of interest (e.g. human population size) to predict future values (the dependent variable). Of the two, it is always the dependent variable whose variation is being studied, by altering inputs, also known as regressors in a statistical context. In an experiment, any variable that can be attributed a value without attributing a value to any other variable is called an i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Linear Regression
In statistics, linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables (also known as dependent and independent variables). The case of one explanatory variable is called '' simple linear regression''; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable. In linear regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Such models are called linear models. Most commonly, the conditional mean of the response given the values of the explanatory variables (or predictors) is assumed to be an affine function of those values; less commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuse ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Publication Bias
In published academic research, publication bias occurs when the outcome of an experiment or research study biases the decision to publish or otherwise distribute it. Publishing only results that show a significant finding disturbs the balance of findings in favor of positive results. The study of publication bias is an important topic in metascience. Despite similar quality of execution and design, papers with statistically significant results are three times more likely to be published than those with null results. This unduly motivates researchers to manipulate their practices to ensure statistically significant results, such as by data dredging. Many factors contribute to publication bias. For instance, once a scientific finding is well established, it may become newsworthy to publish reliable papers that fail to reject the null hypothesis. Most commonly, investigators simply decline to submit results, leading to non-response bias. Investigators may also assume they made a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
