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A list of all pages that have property "What are the best practices?" with value "The most prominent examples in practice are diagnostic studies and hypothesis generating studies. When developing new diagnostic models authors tend to combine multiple prognostic factors and then test such models using the ROC analysis on whole sample without validating the model on a separate sample. However, sometimes the need for validation of model is not disclosed in discussion section. Hypothesis generating studies are usually done on “big data” from databases such as The Cancer Genome Atlas. The primary goal of such studies is to build models based on large data sets and “get the feeling for the data”, or in more technical language to do exploratory data analysis, sometimes such studies do not disclose need for model validation (i.e. confirmatory data analysis). Sometimes after ANOVA, correction for multiple comparison testing also known as post hoc testing is done, these post hoc tests have more stringent statistical significance criteria with the purpose of somewhat replacing model validation. However, replacing model validation with more stringent statistical significance criteria is highly debated topics in a world of statistics. Another case which is usually confused with HARKing are planned multiple comparisons after ANOVA. In this case the fact that comparisons are planned means that model was built before the experiment and based on it, comparisons are done after gathering data.'"`UNIQ--ref-0000024C-QINU`"' '"`UNIQ--references-0000024D-QINU`"'". Since there have been only a few results, also nearby values are displayed.

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Data driven hypotheses without disclosure (‘HARKing’) + (The most prominent examples in practice ar … The most prominent examples in practice are diagnostic studies and hypothesis generating studies. When developing new diagnostic models authors tend to combine multiple prognostic factors and then test such models using the ROC analysis on whole sample without validating the model on a separate sample. However, sometimes the need for validation of model is not disclosed in discussion section. Hypothesis generating studies are usually done on “big data” from databases such as The Cancer Genome Atlas. The primary goal of such studies is to build models based on large data sets and “get the feeling for the data”, or in more technical language to do exploratory data analysis, sometimes such studies do not disclose need for model validation (i.e. confirmatory data analysis). Sometimes after ANOVA, correction for multiple comparison testing also known as post hoc testing is done, these post hoc tests have more stringent statistical significance criteria with the purpose of somewhat replacing model validation. However, replacing model validation with more stringent statistical significance criteria is highly debated topics in a world of statistics. </br></br>Another case which is usually confused with HARKing are planned multiple comparisons after ANOVA. In this case the fact that comparisons are planned means that model was built before the experiment and based on it, comparisons are done after gathering data.'"`UNIQ--ref-0000024C-QINU`"'</br>'"`UNIQ--references-0000024D-QINU`"'NU`"' '"`UNIQ--references-0000024D-QINU`"')

Data driven hypotheses without disclosure (‘HARKing’) +