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Factor analysis of information risk. Factor analysis of information risk (FAIR) is a taxonomy of the factors that contribute to risk and how they affect each other. It is primarily concerned with establishing accurate probabilities for the frequency and magnitude of data loss events. It is not a methodology for performing an enterprise (or ...
An introduction to FAIR data and persistent identifiers. FAIR data is data which meets the FAIR principles of findability, accessibility, interoperability, and reusability (FAIR). [1][2] The acronym and principles were defined in a March 2016 paper in the journal Scientific Data by a consortium of scientists and organizations. [1]
The Pearson's chi-squared test statistic is defined as . The p-value of the test statistic is computed either numerically or by looking it up in a table. If the p-value is small enough (usually p < 0.05 by convention), then the null hypothesis is rejected, and we conclude that the observed data does not follow the multinomial distribution.
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.
Data dredging (also known as data snooping or p-hacking) [1][a] is the misuse of data analysis to find patterns in data that can be presented as statistically significant, thus dramatically increasing and understating the risk of false positives. This is done by performing many statistical tests on the data and only reporting those that come ...
Introduction. FTC Fair Information Practice Principles are the result of the commission's inquiry into the way in which online entities collect and use personal information and safeguards to assure that practice is fair and provides adequate information privacy protection. [2] The FTC has been studying online privacy issues since 1995, and in ...
Confirmatory factor analysis. In statistics, confirmatory factor analysis (CFA) is a special form of factor analysis, most commonly used in social science research. [1] It is used to test whether measures of a construct are consistent with a researcher's understanding of the nature of that construct (or factor).
e. The analysis of competing hypotheses (ACH) is a methodology for evaluating multiple competing hypotheses for observed data. It was developed by Richards (Dick) J. Heuer, Jr., a 45-year veteran of the Central Intelligence Agency, in the 1970s for use by the Agency. [1] ACH is used by analysts in various fields who make judgments that entail a ...