# You can't prove a negative

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The claim "'''you can't prove a negative'''" is often used as a shorthand in discussions to refer to the difficulty of gathering experimental evidence to "prove" that something does not exist. Proving that a phenomenon isn't real takes a lot more time and effort than it takes to demonstrate it. This is especially true when the definition of the phenomenon can be changed at will by its believers. Its very difficult to prove the general non-existence of a phenomenon, and this difficulty is used by believers of many kinds of phenomena to give the appearance of credibility to their beliefs. | The claim "'''you can't prove a negative'''" is often used as a shorthand in discussions to refer to the difficulty of gathering experimental evidence to "prove" that something does not exist. Proving that a phenomenon isn't real takes a lot more time and effort than it takes to demonstrate it. This is especially true when the definition of the phenomenon can be changed at will by its believers. Its very difficult to prove the general non-existence of a phenomenon, and this difficulty is used by believers of many kinds of phenomena to give the appearance of credibility to their beliefs. | ||

− | Much scientific practice has developed to address this issue. In particular, the field of statistics distinguishes between the so-called experimental hypothesis and the null hypothesis. The experimental hypothesis is usually the statement that the scientist would like to investigate the truth of (for example, that the drug under study is an effective treatment), while the null hypothesis is the opposite (that the drug is ineffective). It is possible to prove, by gathering a clinical group together, that the drug has an effect -- but it is impossible to prove that the drug has no effect; it might happen that the drug has an effect, but one too small for that particular experiment to notice (and that a later, larger, or differently run experiment might find it). For this reason, scientists and statisticians refer to a failed experiment as one that "failed to reject the null hypothesis" -- one where all the evidence available was negative, but the null hypothesis is still not "proven. | + | Much scientific practice has developed to address this issue. In particular, the field of statistics distinguishes between the so-called experimental [[hypothesis]] and the null hypothesis. The experimental hypothesis is usually the statement that the scientist would like to investigate the truth of (for example, that the drug under study is an effective treatment), while the null hypothesis is the opposite (that the drug is ineffective). It is possible to prove, by gathering a clinical group together, that the drug has an effect -- but it is impossible to prove that the drug has no effect; it might happen that the drug has an effect, but one too small for that particular experiment to notice (and that a later, larger, or differently run experiment might find it). For this reason, scientists and statisticians refer to a failed experiment as one that "''failed to reject the null hypothesis''" -- one where all the evidence available was negative, but the null hypothesis is still not "proven". |

== See also == | == See also == |

## Revision as of 04:56, 26 April 2007

The claim "**you can't prove a negative**" is often used as a shorthand in discussions to refer to the difficulty of gathering experimental evidence to "prove" that something does not exist. Proving that a phenomenon isn't real takes a lot more time and effort than it takes to demonstrate it. This is especially true when the definition of the phenomenon can be changed at will by its believers. Its very difficult to prove the general non-existence of a phenomenon, and this difficulty is used by believers of many kinds of phenomena to give the appearance of credibility to their beliefs.

Much scientific practice has developed to address this issue. In particular, the field of statistics distinguishes between the so-called experimental hypothesis and the null hypothesis. The experimental hypothesis is usually the statement that the scientist would like to investigate the truth of (for example, that the drug under study is an effective treatment), while the null hypothesis is the opposite (that the drug is ineffective). It is possible to prove, by gathering a clinical group together, that the drug has an effect -- but it is impossible to prove that the drug has no effect; it might happen that the drug has an effect, but one too small for that particular experiment to notice (and that a later, larger, or differently run experiment might find it). For this reason, scientists and statisticians refer to a failed experiment as one that "*failed to reject the null hypothesis*" -- one where all the evidence available was negative, but the null hypothesis is still not "proven".