From Statistical to Practical Significance

Hypothesis testing is one of the most common frameworks for making decisions with data in both scientific and industrial contexts. But this statistical framework, formally called Null Hypothesis Statistics Testing (NHST), can be confusing (and controversial). In an earlier article, we showed how to use the core framework of statistical hypothesis testing: you start with

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How Does Statistical Hypothesis Testing Work?

Statistically significant. p-value. Hypothesis. These terms are not only commonly used in statistics but also have made their way into the vernacular. Making sense of most scientific publications, which can have practical, significant effects on public policy and your life, means understanding a core framework with which we derive much knowledge. That framework is called

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How to Statistically Compare Two Net Promoter Scores

When we wrote Quantifying the User Experience, we put confidence intervals before tests of statistical significance. We generally find fluency with confidence intervals to be easier to achieve and of more value than with formal hypothesis testing. We also teach confidence intervals in our workshops on statistical methods. Most people, even non-researchers, have been exposed

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