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The notable unsolved problems in statistics are generally of a different flavor; according to John Tukey, [1] "difficulties in identifying problems have delayed statistics far more than difficulties in solving problems." A list of "one or two open problems" (in fact 22 of them) was given by David Cox. [2]
In statistics, sampling errors are incurred when the statistical characteristics of a population are estimated from a subset, or sample, of that population. Since the sample does not include all members of the population, statistics of the sample (often known as estimators ), such as means and quartiles, generally differ from the statistics of ...
Her sampling frame might be a list of third-graders in the school district (sampling frame). Over time, it is likely that the researcher will lose track of some of the children used in the original study, so that her sample frame of adults no longer matches the sample frame of children used in the study.
Statistics, when used in a misleading fashion, can trick the casual observer into believing something other than what the data shows. That is, a misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misuse may be accidental. In others, it is purposeful and for the gain of the perpetrator.
Over the ensuing decades, many procedures were developed to address the problem. In 1996, the first international conference on multiple comparison procedures took place in Tel Aviv. [3] This is an active research area with work being done by, for example Emmanuel Candès and Vladimir Vovk.
Sampling error, which occurs in sample surveys but not censuses results from the variability inherent in using a randomly selected fraction of the population for estimation. Nonsampling error, which occurs in surveys and censuses alike, is the sum of all other errors, including errors in frame construction , sample selection, data collection ...
Within statistics, oversampling and undersampling in data analysis are techniques used to adjust the class distribution of a data set (i.e. the ratio between the different classes/categories represented). These terms are used both in statistical sampling, survey design methodology and in machine learning.
Non-sampling errors are other errors which can impact final survey estimates, caused by problems in data collection, processing, or sample design. Such errors may include: Over-coverage: inclusion of data from outside of the population; Under-coverage: sampling frame does not include elements in the population.