Final answer:
Another term for A/B testing is split testing, which is a controlled experiment seeking to test hypotheses about which version of a variable performs better. It involves steps like hypothesis formulation, data collection, and statistical analysis to infer conclusions. Specific statistical distributions and tests like binomial distribution and chi-square test might be applied as appropriate.
Step-by-step explanation:
Another term for A/B testing is split testing. A/B testing is a controlled experiment where two or more versions of a variable are compared against each other to determine which one performs better in terms of predefined metrics. In the context of hypothesis testing, the process involves:
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When performing a hypothesis test on matched or paired samples, it is true that two measurements are drawn from the same pair of individuals or objects and that the two sample means are compared to each other.
For tests involving multiple working hypotheses, like in complex ecological systems, models created are compared against each other as part of a broader scientific approach. This kind of testing allows for the simultaneous comparison of multiple factors affecting the system.
In hypothesis testing scenarios like the taste test example, the binomial distribution or normal approximation to the binomial would be used to see if the outcome significantly deviates from the expected proportion. When testing for independence or mutual exclusivity in data, an appropriate statistical test such as the chi-square test might be used.
Finally, when designing experiments to control for expectancy effects, a placebo might be administered to ensure that the outcomes are not influenced by participant expectations.