Replication increases confidence in results, so start by looking for a systematic review and/or meta-analysis when researching an area. Gabriel Weinberg Information Technology and Services Timi, România 26 urmritori 18 contacte Înscriei-v pentru a v conecta Borbet South Africa Politehnica University, Timisoara.

Any isolated experiment can result in a false positive or a false negative and can also be biased by myriad factors, most commonly selection bias, response bias, and survivorship bias. Recall that in a normal distribution, about 68 percent of values fall within one standard deviation, and 95 percent within two. The normal distribution is particularly useful in experimental analysis due to the central limit theorem. Look for tried-and-true experimental designs, such as randomized controlled experiments or A/B testing, that show statistical significance. Anecdotal evidence and correlations you see in data are good hypothesis generators, but correlation does not imply causation-you still need to rely on well-designed experiments to draw strong conclusions. “Avoid succumbing to the gambler’s fallacy or the base rate fallacy.

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