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Fragility Index
The fragility index is a statistical metric used primarily in the medical literature to assess study results. It denotes the minimum number of subjects whose status would have to change from a non-event to an event in order to convert a statistically significant result of the study into a non-significant result. The smaller the fragility index, the more fragile the result of the study. __TOC__ Derivation The result of scientific studies is said to be statistically significant if the calculated probability of a type I error (assumption of a correlation that does not really exist) is below the arbitrarily chosen but generally accepted limit of 5%. Study results that meet these significance criteria can be published more easily and in more highly ranked journals (publication bias). The sole consideration of the significance level for the assessment of study results is increasingly considered questionable (see also Problems with the Interpretation of Statistical Significance). The ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Measure
In statistics, as opposed to its general use in mathematics, a parameter is any quantity of a statistical population that summarizes or describes an aspect of the population, such as a mean or a standard deviation. If a population exactly follows a known and defined distribution, for example the normal distribution, then a small set of parameters can be measured which provide a comprehensive description of the population and can be considered to define a probability distribution for the purposes of extracting samples from this population. A "parameter" is to a population as a "statistic" is to a sample; that is to say, a parameter describes the true value calculated from the full population (such as the population mean), whereas a statistic is an estimated measurement of the parameter based on a sample (such as the sample mean, which is the mean of gathered data per sampling, called sample). Thus a "statistical parameter" can be more specifically referred to as a population para ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Medical Literature
Medical literature is the scientific literature of medicine: articles in journals and texts in books devoted to the field of medicine. Many references to the medical literature include the health care literature generally, including that of dentistry, veterinary medicine, pharmacy, nursing, and the allied health professions. Contemporary and historic views regarding diagnosis, prognosis and treatment of medical conditions have been documented for thousands of years. The Edwin Smith papyrus is the first known medical treatise. Ancient medical literature often described inflictions related to warfare. History Throughout history, people have written about diseases, how human beings might contract them and what could be done to remedy it. Medicine ranged from folklore and witchcraft to modern evidence-based medicine. Among the most notable early medical descriptions are found in texts from Egypt (''Edwin Smith Papyrus'', ''Ebers Papyrus'', '' Kahun Gynecological Papyrus''), Meso ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sample (statistics)
In this statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a population (statistics), statistical population to estimate characteristics of the whole population. The subset is meant to reflect the whole population, and statisticians attempt to collect samples that are representative of the population. Sampling has lower costs and faster data collection compared to recording data from the entire population (in many cases, collecting the whole population is impossible, like getting sizes of all stars in the universe), and thus, it can provide insights in cases where it is infeasible to measure an entire population. Each observation measures one or more properties (such as weight, location, colour or mass) of independent objects or individuals. In survey sampling, weights can be applied to the data to adjust for the sample design, particularly in stratified samplin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Significance
In statistical hypothesis testing, a result has statistical significance when a result at least as "extreme" would be very infrequent if the null hypothesis were true. More precisely, a study's defined significance level, denoted by \alpha, is the probability of the study rejecting the null hypothesis, given that the null hypothesis is true; and the p-value, ''p''-value of a result, ''p'', is the probability of obtaining a result at least as extreme, given that the null hypothesis is true. The result is said to be ''statistically significant'', by the standards of the study, when p \le \alpha. The significance level for a study is chosen before data collection, and is typically set to 5% or much lower—depending on the field of study. In any experiment or Observational study, observation that involves drawing a Sampling (statistics), sample from a Statistical population, population, there is always the possibility that an observed effect would have occurred due to sampling error al ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Scientific Studies
Scientific study is a creative action to increase knowledge by systematically collecting, interpreting, and evaluating data. According to the hypothetico-deductive paradigm, it should encompass:Popper, Karl (1959). The Logic of Scientific Discovery. Abingdon-on-Thames: Routledge. * The contextualization of the problem; * A hypothesis for explaining the problem considering existing theoretical approaches; * A verification of the hypotheses by an experiment; * Analysis of the test outcome. Scientific study involves scientific theory, scientific method, scientific models, experiments and physical situations. It may refer to: *Scientific method, a body of techniques for investigating phenomena, based on empirical or measurable evidence that is subject to the principles of logic and reasoning *Observational study, draws inferences about the possible effect of a treatment on subjects, where the assignment of subjects into a treated group versus a control group is outside the control of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Type I And Type II Errors
Type I error, or a false positive, is the erroneous rejection of a true null hypothesis in statistical hypothesis testing. A type II error, or a false negative, is the erroneous failure in bringing about appropriate rejection of a false null hypothesis. Type I errors can be thought of as errors of commission, in which the status quo is erroneously rejected in favour of new, misleading information. Type II errors can be thought of as errors of omission, in which a misleading status quo is allowed to remain due to failures in identifying it as such. For example, if the assumption that people are ''innocent until proven guilty'' were taken as a null hypothesis, then proving an innocent person as guilty would constitute a Type I error, while failing to prove a guilty person as guilty would constitute a Type II error. If the null hypothesis were inverted, such that people were by default presumed to be ''guilty until proven innocent'', then proving a guilty person's innocence would ... [...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 Statistical significance, 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 of experiments, 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 ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Significance Level
In statistical hypothesis testing, a result has statistical significance when a result at least as "extreme" would be very infrequent if the null hypothesis were true. More precisely, a study's defined significance level, denoted by \alpha, is the probability of the study rejecting the null hypothesis, given that the null hypothesis is true; and the ''p''-value of a result, ''p'', is the probability of obtaining a result at least as extreme, given that the null hypothesis is true. The result is said to be ''statistically significant'', by the standards of the study, when p \le \alpha. The significance level for a study is chosen before data collection, and is typically set to 5% or much lower—depending on the field of study. In any experiment or observation that involves drawing a sample from a population, there is always the possibility that an observed effect would have occurred due to sampling error alone. But if the ''p''-value of an observed effect is less than (or equal to ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dichotomy
A dichotomy () is a partition of a set, partition of a whole (or a set) into two parts (subsets). In other words, this couple of parts must be * jointly exhaustive: everything must belong to one part or the other, and * mutually exclusive: nothing can belong simultaneously to both parts. If there is a concept A, and it is split into parts B and not-B, then the parts form a dichotomy: they are mutually exclusive, since no part of B is contained in not-B and vice versa, and they are jointly exhaustive, since they cover all of A, and together again give A. Such a partition is also frequently called a bipartition. The two parts thus formed are Complement (set theory), complements. In logic, the partitions are dual (category theory), opposites if there exists a proposition such that it holds over one and not the other. Treating continuous variables or multicategorical variables as binary variables is called discretization, dichotomization. The discretization error inherent in dichoto ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Intensive Care Medicine
Intensive care medicine, usually called critical care medicine, is a medical specialty that deals with seriously or critically ill patients who have, are at risk of, or are recovering from conditions that may be life-threatening. It includes providing life support, invasive Monitoring (medicine), monitoring techniques, resuscitation, and end-of-life care. Doctors in this specialty are often called intensive care physicians, critical care physicians, or intensivists. Intensive care relies on multidisciplinary teams composed of many different health professionals. Such teams often include doctors, nurses, Physical therapy, physical therapists, respiratory therapists, and pharmacists, among others. They usually work together in intensive care units (ICUs) within a hospital. Scope Patients are admitted to the intensive care unit if their medical needs are greater than what the general hospital ward can provide. Indications for the ICU include blood pressure support for cardiova ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Biostatistics
Biostatistics (also known as biometry) is a branch of statistics that applies statistical methods to a wide range of topics in biology. It encompasses the design of biological experiments, the collection and analysis of data from those experiments and the interpretation of the results. History Biostatistics and genetics Biostatistical modeling forms an important part of numerous modern biological theories. Genetics studies, since its beginning, used statistical concepts to understand observed experimental results. Some genetics scientists even contributed with statistical advances with the development of methods and tools. Gregor Mendel started the genetics studies investigating genetics segregation patterns in families of peas and used statistics to explain the collected data. In the early 1900s, after the rediscovery of Mendel's Mendelian inheritance work, there were gaps in understanding between genetics and evolutionary Darwinism. Francis Galton tried to expand Mendel's ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
