The null hypothesis (H0) often represents a skeptical perspective or a claim to be tested. The alternative hypothesis (HA) represents an alternative claim under consideration and is often represented by a range of possible parameter values.
Our job as data scientists is to play the role of a skeptic: before we buy into the alternative hypothesis, we need to see strong supporting evidence.
The null hypothesis often represents a skeptical position or a perspective of “no difference”. In our first example, we’ll consider whether the typical person does any different than random guessing on Roslings’ question about infant vaccinations.
The alternative hypothesis generally represents a new or stronger perspective. In the case of the question about infant vaccinations, it would certainly be interesting to learn whether people do better than random guessing, since that would mean that the typical person knows something about world health statistics. It would also be very interesting if we learned that people do worse than random guessing, which would suggest people believe incorrect information about world health.
The hypothesis testing framework is a very general tool, and we often use it without a second thought. If a person makes a somewhat unbelievable claim, we are initially skeptical. However, if there is sufficient evidence that supports the claim, we set aside our skepticism and reject the null hypothesis in favor of the alternative. The hallmarks of hypothesis testing are also found in the US court system.
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