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Data manipulation is a serious issue/consideration in the most honest of statistical analyses. Outliers, missing data and non-normality can all adversely affect the validity of statistical analysis. It is appropriate to study the data and repair real problems before analysis begins.
Necessary Condition Analysis (NCA) offers a nuanced perspective on data analysis by identifying conditions that must be present for a desired outcome to occur. However, its utility is bounded by several limitations that users must consider. Primarily, NCA's insights are limited by the quality and scope of the data used.
Despite these limitations, backtesting provides information not available when models and strategies are tested on synthetic data. Historically, backtesting was only performed by large institutions and professional money managers due to the expense of obtaining and using detailed datasets.
Hypothesis testing, though, is a dominant approach to data analysis in many fields of science. Extensions to the theory of hypothesis testing include the study of the power of tests, i.e. the probability of correctly rejecting the null hypothesis given that it is false.
Multiple testing procedures are sometimes used to compensate, but that is often difficult or impossible to do precisely. Post hoc analysis that is conducted and interpreted without adequate consideration of this problem is sometimes called data dredging by critics because the statistical associations that it finds are often spurious. [4]
Cross-validation, [2] [3] [4] sometimes called rotation estimation [5] [6] [7] or out-of-sample testing, is any of various similar model validation techniques for assessing how the results of a statistical analysis will generalize to an independent data set.
Test data are sets of inputs or information used to verify the correctness, performance, and reliability of software systems. Test data encompass various types, such as positive and negative scenarios, edge cases, and realistic user scenarios, and aims to exercise different aspects of the software to uncover bugs and validate its behavior.
A normal quantile plot for a simulated set of test statistics that have been standardized to be Z-scores under the null hypothesis. The departure of the upper tail of the distribution from the expected trend along the diagonal is due to the presence of substantially more large test statistic values than would be expected if all null hypotheses were true.