Hypothesis testing. Null vs alternative

Hypothesis testing. Null vs alternative

There are four steps in data-driven decision-making. First, you must formulate a hypothesis. Second, once you have formulated a hypothesis, you will have to find the right test for your hypothesis. Third, you execute the test. And fourth, you make a decision based on the result. Let’s start from the beginning. What is a hypothesis? Though there are many ways to define it, the most intuitive I’ve seen is: “A hypothesis is an idea that can be tested.” This is not the formal definition, but it explains the point very well. So, if I tell you that apples in New York are expensive, this is an idea, or a statement, but is not testable, until I have something to compare it with. For instance, if I define expensive as: any price higher than $1.75 dollars per pound, then it immediately becomes a hypothesis. Alright, what’s something that cannot be a hypothesis? An example may be: would the USA do better or worse under a Clinton administration, compared to a Trump administration? Statistically speaking, this is an idea, but there is no data to test it, therefore it cannot be a hypothesis of a statistical test. Actually, it is more likely to be a topic of another discipline. Conversely, in statistics, we may compare different US presidencies that have already been completed, such as the Obama administration and the Bush administration, as we have data on both. Generally, the researcher is trying to reject the null hypothesis. Think about the null hypothesis as the status quo and the alternative as the change or innovation that challenges that status quo. In our example, Paul was representing the status quo, which we were challenging. Facebook: http://bit.ly/2BEaHRB Twitter: https://twitter.com/365datascience LinkedIn: http://bit.ly/2QOMrGW Google+: http://bit.ly/2BR1upt
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