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Georgi Georgiev
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Going beyond #statistics and into the design of experiments, more precisely online controlled experiments (a.k.a. A/B tests). Can your test result be highly statistically significant and still be biased or completely misleading when you implement it? Can you lose money even if you get all the stats right? A detailed look into external validity / generalizability and what one can do to increase the chance of conducting tests which result in useful predictions.

#conversionrate #cro #abtesting

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Going beyond #statistics and into the design of experiments, more precisely online controlled experiments (a.k.a. A/B tests). Can your test result be highly statistically significant and still be biased or completely misleading when you implement it? Can you lose money even if you get all the stats right? A detailed look into external validity / generalizability and what one can do to increase the chance of conducting tests which result in useful predictions.

#conversionrate #cro #abtesting

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Going beyond #statistics and into the design of experiments, more precisely online controlled experiments (a.k.a. A/B tests). Can your test result be highly statistically significant and still be biased or completely misleading when you implement it? Can you lose money even if you get all the stats right? A detailed look into external validity / generalizability and what one can do to increase the chance of conducting tests which result in useful predictions.

#conversionrate #cro #abtesting

Post has attachment
Going beyond #statistics and into the design of experiments, more precisely online controlled experiments (a.k.a. A/B tests). Can your test result be highly statistically significant and still be biased or completely misleading when you implement it? Can you lose money even if you get all the stats right? A detailed look into external validity / generalizability and what one can do to increase the chance of conducting tests which result in useful predictions.

#conversionrate #cro #abtesting

Post has attachment
Going beyond #statistics and into the design of experiments, more precisely online controlled experiments (a.k.a. A/B tests). Can your test result be highly statistically significant and still be biased or completely misleading when you implement it? Can you lose money even if you get all the stats right? A detailed look into external validity / generalizability and what one can do to increase the chance of conducting tests which result in useful predictions.

#conversionrate #cro #abtesting

Post has attachment
Going beyond #statistics and into the design of experiments, more precisely online controlled experiments (a.k.a. A/B tests). Can your test result be highly statistically significant and still be biased or completely misleading when you implement it? Can you lose money even if you get all the stats right? A detailed look into external validity / generalizability and what one can do to increase the chance of conducting tests which result in useful predictions.

#conversionrate #cro #abtesting
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Many, including PhDs in #statistics, can't wrap their heads around the paradox of 1-tailed v. 2-tailed tests. Given data X, how can we reject with #probability P the claim of "no difference or negative difference", but not the claim of "no difference"? Here is my solution: https://www.onesided.org/articles/the-paradox-of-one-sided-v-two-sided-tests-of-significance.php
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An article I promised several weeks ago is now ready. Learn how the poor design of a couple of statistical tables in the early 20-th century likely contributed to a major misunderstanding of a whole class of statistical calculations. This usability/UX issue is then transferred to modern statistical software and contributes to poor statistical practices in medicine, a lot of behavioral sciences, economics and business risk estimation included...

https://www.onesided.org/articles/widespread-usage-of-two-sided-tests-result-of-usability-issue.php
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