# Portal:Statistics

When census data cannot be collected, statisticians collect data by developing specific experiment designs and survey samples. Representative sampling assures that inferences and conclusions can reasonably extend from the sample to the population as a whole. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation. Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation). Descriptive statistics are most often concerned with two sets of properties of a A standard statistical procedure involves the test of the relationship between two statistical data sets, or a data set and synthetic data drawn from an idealized model. A hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. Rejecting or disproving the null hypothesis is done using statistical tests that quantify the sense in which the null can be proven false, given the data that are used in the test. Working from a null hypothesis, two basic forms of error are recognized: Type I errors (null hypothesis is falsely rejected giving a "false positive") and Type II errors (null hypothesis fails to be rejected and an actual difference between populations is missed giving a "false negative"). Multiple problems have come to be associated with this framework: ranging from obtaining a sufficient sample size to specifying an adequate null hypothesis. Measurement processes that generate statistical data are also subject to error. Many of these errors are classified as random (noise) or systematic (bias), but other types of errors (e.g., blunder, such as when an analyst reports incorrect units) can also be important. The presence of missing data or censoring may result in biased estimates and specific techniques have been developed to address these problems. Statistics can be said to have begun in ancient civilization, going back at least to the 5th century BC, but it was not until the 18th century that it started to draw more heavily from calculus and probability theory. In more recent years statistics has relied more on statistical software to produce tests such as descriptive analysis.
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t-test and Student's t-distribution. He joined the Dublin brewery of Arthur Guinness & Son in 1899, where he applied his statistical knowledge both in the brewery and on the farm to the selection of the best yielding varieties of barley. Gosset's key 1908 papers addressed the brewer's concern with small samples. To prevent further disclosure of confidential information, Guinness prohibited its employees from publishing any papers regardless of the contained information, so Gosset used the pseudonym Student for his publications to avoid their detection by his employer.
These are featured or good articles on statistics topics. Featured articles
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- AP Statistics
- Muthu Alagappan
- Algorithmic bias
- Ars Conjectandi
- Fleiss' kappa
- Hidden Markov model
- JMP (statistical software)
- Maximum spacing estimation
- Metallic Metals Act
- Nearest-neighbor chain algorithm
- Dorothy P. Rice
- SAS (software)
- SAS Institute
- Shapley–Folkman lemma
- Francis Amasa Walker
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- Game theory
- Numbers
- Probability
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- ... that Gustav Elfving invented the optimal design of experiments, and so minimized the cost of a cartographic survey, while trapped in his tent in storm-ridden Greenland?
- ... that in 2009, Revolution Analytics named Norman H. Nie, one of the original SPSS developers, as their new CEO?
- ... that proper design of a sampling frame can be crucial in statistical research?
- ... that least-squares spectral analysis is a method for estimating a frequency spectrum, based on a least squares fit between data and trigonometric functions?
- ... that the Holtsmark distribution was proposed in 1919 as a model for the gravitational field of stars?
- ... that variables and attributes are some of the most basic concepts in science?
- ... that although randomness had long been viewed as an obstacle, it is now used as a tool for designing better algorithms?
- ... that the Jadad scale is the world's most widely used means of assessing the methodological quality of clinical trials?
- ... that Ulpian's life table predicted a life expectancy of 19 to 23 years for citizens of the Roman Empire?
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