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Data analysis is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users. [1] Data is collected and analyzed to answer questions, test hypotheses, or disprove theories. [11] Statistician John Tukey, defined data analysis in 1961, as:
If the data collection or analysis were to stop at a point where the p-value happened to fall below the significance level, a spurious statistically significant difference could be reported. Optional stopping is a practice where one collects data until some stopping criteria is reached. While it is a valid procedure, it is easily misused.
Henry Scheffé's simultaneous test of all contrasts in multiple comparison problems is the most [citation needed] well-known remedy in the case of analysis of variance. [1] It is a method designed for testing hypotheses suggested by the data while avoiding the fallacy described above.
The Data Insights section includes five question types: table analysis, graphics interpretation, multi-source reasoning, two-part analysis, and data sufficiency. In the table analysis section, test takers are presented with a sortable table of information, similar to a spreadsheet, which has to be analyzed.
The typical steps involved in performing a frequentist hypothesis test in practice are: Define a hypothesis (claim which is testable using data). Select a relevant statistical test with associated test statistic T. Derive the distribution of the test statistic under the null hypothesis from the assumptions.
Finally, the test data set is a data set used to provide an unbiased evaluation of a final model fit on the training data set. [5] If the data in the test data set has never been used in training (for example in cross-validation), the test data set is also called a holdout data set. The term "validation set" is sometimes used instead of "test ...
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