Portal:Statistics

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Portal:Statistics

## Welcome to the statistics portal
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. ## Selected articleThe A well-known statement of the problem was published in Because there is no way for the player to know which of the two remaining unopened doors is the winning door, most people assume that each of these doors has an equal probability and conclude that switching does not matter. In fact, the player should switch - doing so doubles the probability of winning the car from 1/3 to 2/3. When the problem and the solution appeared in ## Selected biography
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A ## Did you know?- ...that the term
*bias*is not necessarily pejorative in statistics, since biased estimators may have desirable properties (such as a smaller mean squared error than any unbiased estimator), and that in extreme cases the only unbiased estimators are not even within the convex hull of the parameter space? - ...that William Sealy Gosset published under the pseudonym
*Student*in order to avoid detection by his employer, and so his most famous achievement is now referred to as Student's t-distribution, which might otherwise have been Gosset's t-distribution? - ...that in 1747, by dividing 12 men suffering from scurvy into six pairs and giving each group different additions to their basic diet for a period of two weeks, the surgeon James Lind conducted one of the first controlled experiments?
- ...that the Cauchy distribution is an example of a distribution which has no mean, variance or higher moments defined?
- ...that according to Benford's law, the first digit from many real-life sources of data is 1 almost one third of the time?
- ...that the Law of Truly Large Numbers of Diaconis and Mosteller states that with a sample size large enough, any outrageous thing is likely to happen?
- ...that for the number of shuffles needed to randomize a deck, Persi Diaconis concluded that for good shuffling technique, the deck did not start to become random until five good riffle shuffles, and was truly random after seven, in the precise sense of variation distance described in Markov chain mixing time?
- ...that for many standard probability distributions, there are infinitely many outcomes in the sample space, so that attempting to define probabilities for all possible subsets of such spaces would cause difficulties for 'badly-behaved' sets such as those which are nonmeasurable?
- ... that Jan Pieka?kiewicz, a leading Polish statistician, became the Polish Underground State's Government Delegate, and died at the hands of Nazi Germany?
## Topics in Statistics
## Statistics categoriesClick an arrow symbol to expand any of the sub-categories: ## Associated Wikimedia |

This article uses material from the Wikipedia page available here. It is released under the Creative Commons Attribution-Share-Alike License 3.0.

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