# Data generating process

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In statistics and in empirical sciences, a '''data generating process''' is a process in the real world that "generates" the data one is interested in.<ref name="TuZhou2004">{{Cite journal|title=Data-generating process uncertainty: What difference does it make in portfolio decisions?|journal=Journal of Financial Economics|volume=72|issue=2|pages=385–421|year=2004|doi=10.1016/j.jfineco.2003.05.003|url=https://www.sciencedirect.com/science/article/pii/S0304405X03002472|last1=Tu|first1=Jun|last2=Zhou|first2=Guofu|url-access=subscription}}</ref> This process encompasses the underlying mechanisms, factors, and randomness that contribute to the production of observed data. Usually, scholars do not know the real data generating [model](/source/model) and instead rely on assumptions, approximations, or inferred models to analyze and interpret the observed data effectively. However, it is assumed that those real models have observable consequences. Those consequences are the [distributions](/source/Frequency_distribution) of the data in the [population](/source/Statistical_population). Those distributors or models can be represented via [mathematical function](/source/mathematical_function)s. There are many functions of data distribution. For example, [normal distribution](/source/normal_distribution), [Bernoulli distribution](/source/Bernoulli_distribution), [Poisson distribution](/source/Poisson_distribution), etc.

==References==
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* https://stats.stackexchange.com/questions/443320/what-does-a-data-generating-process-dgp-actually-mean

Category:Probability distributions

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