Conservative<\/strong>: Reasonable version of stuffy\u2014appropriately cautious and less afraid of criticism.<\/li>\n<\/ul>\nThinking about these styles and their implications for research in different contexts can be helpful for researchers who use statistical analysis to guide their research narratives.<\/p>\n
A conservative style is appropriate in some research contexts (e.g., research on medical devices). However, in a typical UX research context, avoiding Type II errors (false negatives) can be as important as avoiding Type I errors (false positives), so a more liberal style is warranted.<\/p>\n
Based on stylistic practices that include (1) consistency within related studies but a willingness to be liberal or conservative depending on the research context and (2) a focus on practical rather than just statistical significance, we\u2019ve described a fifth style between liberal and conservative\u2014the pragmatic style.<\/p>\n
<\/p>\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
When learning statistics, you\u2019ll encounter many formulas based on principles of probability and mathematics. But statistics isn\u2019t just a formulaic process where you enter data and are told what to do. Statistics should guide, not dictate, decisions. In making decisions, though, there are different styles of interpreting data. 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