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:

- Identify the question
- Determine the significance
- Choose the test
- Interpret the results
- 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

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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