The **null hypothesis** (H_{0}) is the hypothesis that states there is no statistical difference between two sample sets. In other words, it assumes the independent variable does not have an effect on the dependent variable in a scientific experiment.

The null hypothesis is the most powerful type of hypothesis in the scientific method because it’s the easiest one to test with a high confidence level using statistics. If the null hypothesis is accepted, then it’s evidence any observed differences between two experiment groups are due to random chance. If the null hypothesis is rejected, then it’s strong evidence there is a true difference between test sets or that the independent variable affects the dependent variable.

### Key Points

- The null hypothesis is a nullifiable hypothesis. A researcher seeks to reject it because this result strongly indicates observed differences are real and not just due to chance.
- The null hypothesis may be accepted or rejected, but not proven. There is always a level of confidence in the outcome.

### What Is the Null Hypothesis?

The null hypothesis is written as H_{0}, which is read as H-zero, H-nought, or H-null. It is associated with another hypothesis, called the alternate or alternative hypothesis H_{A} or H_{1}. When the null hypothesis and alternate hypothesis are written mathematically, they cover all possible outcomes of an experiment.

An experimenter tests the null hypothesis with a statistical analysis called a significance test. The significance test determines the likelihood that the results of the test are not due to chance. Usually, a researcher uses a confidence level of 95% or 99% (p-value of 0.05 or 0.01). But, even if the confidence in the test is high, there is always a small chance the outcome is incorrect. This means you can’t prove a null hypothesis. It’s also a good reason why it’s important to repeat experiments.

### Exact and Inexact Null Hypothesis

The most common type of null hypothesis assumes no difference between two samples or groups or no measurable effect of a treatment. This is the **exact hypothesis**. If you’re asked to state a null hypothesis for a science class, this is the one to write. It is the easiest type of hypothesis to test and is the only one accepted for certain types of analysis. Examples include:

There is no difference between two groups*H*_{0}: μ_{1} = μ_{2} (where *H*_{0} = the null hypothesis, μ_{1} = the mean of population 1, and μ_{2} = the mean of population 2)

Both groups have value of 100 (or any number or quality)*H*_{0}: μ = 100

However, sometimes a researcher may test an **inexact hypothesis**. This type of hypothesis specifies ranges or intervals. Examples include:

Recovery time from a treatment is the same or worse than a placebo:*H*_{0}: μ ≥ placebo time

There is a 5% or less difference between two groups:*H*_{0}: 95 ≤ μ ≤ 105

An inexact hypothesis offers “directionality” about a phenomenon. For example, an exact hypothesis can indicate whether or not a treatment has an effect, while an inexact hypothesis can tell whether an effect is positive of negative. However, an inexact hypothesis may be harder to test and **some scientists and statisticians disagree about whether it’s a true null hypothesis**.

### How to State the Null Hypothesis

To state the null hypothesis, first state what you expect the experiment to show. Then, rephrase the statement in a form that assumes there is no relationship between the variables or that a treatment has no effect.

Example: A researcher tests whether a new drug speeds recovery time from a certain disease. The average recovery time without treatment is 3 weeks.

- State the goal of the experiment: “I hope the average recovery time with the new drug will be less than 3 weeks.”
- Rephrase the hypothesis to assume the treatment has no effect: “If the drug doesn’t shorten recovery time, then the average time will be 3 weeks or longer.” Mathematically:

H_{0}: μ ≥ 3

This null hypothesis (inexact hypothesis) covers both the scenario in which the drug has no effect and the one in which the drugs makes the recovery time longer. The alternate hypothesis is that average recovery time will be less than three weeks:

H_{A}: μ < 3

Of course, the researcher could test the no-effect hypothesis (exact null hypothesis):

H_{0}: μ = 3

The danger of testing this hypothesis is that rejecting it only implies the drug affected recovery time (not whether it made it better or worse). This is because the alternate hypothesis is:

H_{A}: μ ≠ 3 (which includes μ <3 and μ >3)

Even though the no-effect null hypothesis yields less information, it’s used because it’s easier to test using statistics. Basically, testing whether something is unchanged/changed is easier than trying to quantify the nature of the change.

### Null Hypothesis Examples

Remember, a researcher hopes to reject the null hypothesis because this supports the alternate hypothesis. Also, be sure the null and alternate hypothesis cover all outcomes. Finally, remember a simple true/false, equal/unequal, yes/no exact hypothesis is easier to test than a more complex inexact hypothesis.

Question | Null Hypothesis | Alternate Hypothesis |

Does chewing willow bark relieve pain? | Pain relief is the same compared with a placebo. (exact) Pain relief after chewing willow bark is the same or worse versus taking a placebo. (inexact) | Pain relief is different compared with a placebo. (exact) Pain relief is better compared to a placebo. (inexact) |

Do cats care about the shape of their food? | Cats show no food preference based on shape. (exact) | Cat show a food preference based on shape. (exact) |

Do teens use mobile devices more than adults? | Teens and adults use mobile devices the same amount. (exact) Teens use mobile devices less than or equal to adults. (inexact) | Teens and adults used mobile devices different amounts. (exact) Teens use mobile devices more than adults. (inexact) |

Does the color of light influence plant growth? | The color of light has no effect on plant growth. (exact) | The color of light affects plant growth. (exact) |

### References

- Adèr, H. J.; Mellenbergh, G. J. & Hand, D. J. (2007).
*Advising on Research Methods: A Consultant’s Companion*. Huizen, The Netherlands: Johannes van Kessel Publishing. ISBN 978-90-79418-01-5. - Cox, D. R. (2006).
*Principles of Statistical Inference*. Cambridge University Press. ISBN 978-0-521-68567-2. - Everitt, Brian (1998).
*The Cambridge Dictionary of Statistics*. Cambridge, UK New York: Cambridge University Press. ISBN 978-0521593465. - Weiss, Neil A. (1999).
*Introductory Statistics*(5th ed.). ISBN 9780201598773.