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Often, the methods employed are unique to specific agencies and organizations. For example, the United States Census Bureau has developed models using the U.S. Postal Service's Delivery Sequence File, IRS 1040 address data, commercially available foreclosure counts, and other data to develop models capable of predicting undercount by census block.
Note that data dredging is a valid way of finding a possible hypothesis but that hypothesis must then be tested with data not used in the original dredging. The misuse comes in when that hypothesis is stated as fact without further validation. "You cannot legitimately test a hypothesis on the same data that first suggested that hypothesis.
Non-sampling errors in survey estimates can arise from: [3] Coverage errors, such as failure to accurately represent all population units in the sample, or the inability to obtain information about all sample cases; Response errors by respondents due for example to definitional differences, misunderstandings, or deliberate misreporting;
Data editing is defined as the process involving the review and adjustment of collected survey data. [1] Data editing helps define guidelines that will reduce potential bias and ensure consistent estimates leading to a clear analysis of the data set by correct inconsistent data using the methods later in this article. [2]
The U.S. National Center for Health Statistics, for example, deliberately oversamples from minority populations in many of its nationwide surveys in order to gain sufficient precision for estimates within these groups. [15] These surveys require the use of sample weights (see later on) to produce proper estimates across all ethnic groups.
Statistical bias exists in numerous stages of the data collection and analysis process, including: the source of the data, the methods used to collect the data, the estimator chosen, and the methods used to analyze the data. Data analysts can take various measures at each stage of the process to reduce the impact of statistical bias in their ...
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In survey-type situations, these errors can be mistakes in the collection of data, including both the incorrect recording of a response and the correct recording of a respondent's inaccurate response.