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In the future, the data, assumptions, and analysis tools on which today's operational decisions rely will no longer be fast or accurate enough to reliably manage a system operated closer to the reliability limits.
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Robustness refers to "the sensitivity of the overall conclusions to various limitations of the data, assumptions, and analytic approaches to data analysis" [ 8].
For future studies, standard reporting should include specific data assumptions for analysis.
Missing data patterns and assumptions will be described, and the potential impact of alternative missing data assumptions on the primary analysis will be explored in sensitivity analyses.
Data sampling and analysis are organized according to a scale-dependent set of assumptions allowing a problem-oriented approach.
Data management and analysis vague or unsophisticated.
Data collection and analysis were manual.
Data compilation and analysis.
In addition, sensitivity analysis considering plausible models and missing data assumptions will be used to check the robustness of results across possible missing data assumptions.
This real data results further verify our assumptions and theoretical analysis.
For the t test, Livingston [ 23] presented a list of conditions that must be considered (e.g., normal distribution, equal variances, etc).. Consequently, some researchers routinely check if their data fulfill the assumptions and change the analysis method if they do not (for a review, see [ 24]).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com