Cuantificando a un líder

La mirada de un líder Llegar a la cima es como mucho que ver con cómo se mire como lo que consigues The Economist En la sociedad GORILA, el poder pertenece al Espalda Plateadas varones. Estas espléndidas criaturas tienen numerosos marcadores de estado, además de su cabello hacia atrás: son más grandes que el resto de su banda, hacen posturas que llenan el espacio, producen sonidos más profundos, golpean sus pechos vigorosamente y, en general, exudan un aire de aptitud física. Las cosas no son tan diferentes en el mundo corporativo. El ejecutivo típico jefe es más de seis pies de altura, tiene una voz profunda y una buena postura, un toque de gris en su pelo grueso, brillante y, por su edad, un cuerpo en forma. Los jefes se esparcieron...

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Pruebas A/B sin desvío estándar y como solucionarlas

A/B Testing Duration Data By Evan Miller Let's say you make a change to your website and want to test whether people tend to stay on the site longer after the change. You might think: that's easy! I'll just compare the average visit lengths before and after the change and then I'll have my answer. Readers of this blog are, of course, savvier than that; they know they should perform a proper statistical test to determine if a reported difference could be due to chance. But there's a problem. When comparing two continuous quantities (such as visit durations), the usual statistical test is the two sample t-test. A t-test requires three key pieces of information from each test group: the number of subjects, the sample mean, and...

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Cómo nunca correr una prueba A/B...

How Not To Run An A/B Test By Evan Miller If you run A/B tests on your website and regularly check ongoing experiments for significant results, you might be falling prey to what statisticians call repeated significance testing errors. As a result, even though your dashboard says a result is statistically significant, there’s a good chance that it’s actually insignificant. This note explains why. Background When an A/B testing dashboard says there is a “95% chance of beating original” or “90% probability of statistical significance,” it’s asking the following question: Assuming there is no underlying difference between A and B, how often will we see a difference like we do in the data just by chance? The answer to that...

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