Overview of Hypothesis Testing

Overview of Hypothesis Testing
Overview of Hypothesis Testing

Overview of Hypothesis Testing. Photo by Hartwig HDK

You may know all the statistics in the world, but if you jump straight from those statistics to the wrong conclusion, you could end up making a multi-million dollar error. That’s where hypothesis testing comes in. It combines tried-and-tested analysis tools, real-world data, and a framework that allows us to test our assumptions and beliefs. This way, we can say how likely something is to be true or not within a given standard of accuracy.

When using hypothesis testing, we create:

  • A null hypothesis (H0): the assumption that the experimental results are due to chance alone; nothing (from 6M) influenced our results.
  • An alternative hypothesis (Ha): we expect to find a particular outcome.

These hypotheses should always be mutually exclusive: if one is true, the other is false.

Once we have our null and alternative hypotheses, we test them with a sample of an entire population, check our results, and come up with a conclusion based on those results.

Note: A NULL hypothesis is never accepted; we simply fail to reject it. We are always testing the NULL.

Basic Hypothesis Testing Process

The basic hypothesis testing process consists of five steps:

  1. Identify the question
  2. Determine the significance
  3. Choose the test
  4. Interpret the results
  5. Make a decision.

Read more about the hypothesis testing process.

Hypothesis Testing Terminology

There is a lot of specialist terminology used in the field of hypothesis testing. We’ve collated a list of the most common terms and their meanings, for easy lookup. See the hypothesis testing terminology list.

Tailed Hypothesis Tests

Hypothesis tests are commonly referred to according to their ‘tails’, or the critical regions that exist within them. There are three basic types: right-tailed, left-tailed, and two-tailed. Read more about tailed hypothesis tests.

Errors in Hypothesis Testing

When we talk about an error in the context of hypothesis testing, the word has a very specific meaning: it refers to incorrectly either rejecting or accepting a hypothesis. Read more about errors in hypothesis testing.


P-values are calculated values that we use to work out how statistically significant our test results are, and how probable it is that we’ll make an error. Read more about p-values.

Types of Hypothesis Tests

One aspect of hypothesis testing that can confuse the new student is exactly which test – out of a large number of available tests – is correct to use. We run through the types of hypothesis tests, and give a brief explanation of what each one is commonly used for. Read more about types of hypothesis tests.

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