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For over a half century, various fields in the behavioral and social sciences have debated the appropriateness of null hypothesis significance testing (NHST) in the presentation and assessment of research results. A long list of criticisms has fueled the so-called significance testing controversy. The conventional NHST framework encourages researchers to devote excessive attention to statistical significance while underemphasizing practical (e.g., scientific, substantive, social, political) significance. I introduce a simple, intuitive approach that grounds testing in subject-area expertise, balancing the dual concerns of detectability and importance. The proposed practical and statistical significance test allows the social scientist to test for real-world significance, taking into account both sampling error and an assessment of what parameter values should be deemed interesting, given theory. The matter of what constitutes practical significance is left in the hands of the researchers themselves, to be debated as a natural component of inference and interpretation. [ABSTRACT FROM AUTHOR]

Political scientists often theorize that an explanatory variable should have 'no effect' and support this claim by demonstrating that its coefficient's estimate is not statistically significant. This empirical argument is quite weak, but I introduce applied researchers to simple, powerful tools that can strengthen their arguments for this hypothesis. With several supporting examples, I illustrate that researchers can use 90% confidence intervals to argue against meaningful effects and provide persuasive evidence for their hypothesis. [ABSTRACT FROM AUTHOR]