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			== Missing_data ==
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			File:Missing_not_at_random.png\|Missing not at random
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Prab: CSV import

2024-03-02T02:14:31Z

CSV import

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'''Missing data''' refers to the absence of [[data]] values in a dataset. This can occur in any type of data collection process, including [[survey]]s, [[experiment]]s, and [[observational studies]]. Missing data can lead to a loss of statistical power, biased estimates, and invalid conclusions.

==Types of Missing Data==

There are three main types of missing data:

* '''[[Missing Completely at Random (MCAR)]]''': This occurs when the probability of missing data on a variable is the same for all samples. In other words, the missingness is independent of both observed and unobserved data.

* '''[[Missing at Random (MAR)]]''': This occurs when the probability of missing data on a variable is related to some other observed data, but not the missing data itself.

* '''[[Not Missing at Random (NMAR)]]''': This occurs when the probability of missing data on a variable is related to the missing data itself, even after controlling for other observed data.

==Handling Missing Data==

There are several methods for handling missing data, including:

* '''[[Listwise deletion]]''': This method involves removing any case with at least one missing value. While simple to implement, it can lead to a significant reduction in sample size and potential bias.

* '''[[Pairwise deletion]]''': This method involves using all available data for each analysis. It maximizes the use of available data, but can lead to inconsistent results.

* '''[[Imputation]]''': This method involves replacing missing values with estimated ones. There are many imputation methods, including mean imputation, regression imputation, and multiple imputation.

* '''[[Maximum likelihood estimation]]''': This method involves using a statistical model to estimate the parameters of the data, taking into account the missing data.

==Implications of Missing Data==

Missing data can have serious implications for data analysis. It can lead to biased estimates, reduced statistical power, and invalid conclusions. Therefore, it is important to carefully consider and address missing data in any data analysis.

==See Also==

* [[Data cleaning]]
* [[Data preprocessing]]
* [[Data imputation]]
* [[Statistical analysis]]

[[Category:Statistics]]
[[Category:Data analysis]]
[[Category:Data preprocessing]]
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Missing data - Revision history

Prab: CSV import

Prab: CSV import