Key Insights

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  Statistical significance often confuses practitioners because it requires mentally flipping hypotheses and disproving nulls, which is cognitively demanding.

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  Effect size is critical to understanding whether a change detected by statistics is meaningful in practice, a concept often neglected in statistics education.

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  Fast progress isn't necessarily good progress; teams benefit from slowing down and using statistics to ensure they're moving in the right direction.

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  Engineers can be reluctant to implement experiments due to the extra coding load, but they respond well when they understand the learning value gained.

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  Survey results (the numerical outcomes) are often confused with the number of respondents required for statistical significance, leading to misunderstandings.

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  Statistical thresholds like 95% confidence can be adjusted depending on project needs; lower confidence levels are sometimes acceptable.

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  A good hypothesis often starts as an intuitive guess, which gets refined over time through repeated testing and data collection.

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  Qualitative and quantitative research should be viewed as complementary tools in a holistic research approach rather than opposed methods.

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  AI tools can help generate first drafts of survey questions, but human-centered pilot testing is essential to avoid errors and misinterpretations.

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  It's common and acceptable to act on results that are significant in practice but not statistically significant, especially when outcomes clearly affect users.

Notable Quotes