hypothesis testing

hypothesis testing

Hypothesis testing

is a statistical inferencial procedure in which a statement based on some experimental or observational study is formulated, tested, then put through a decision process. The decision process either accepts or rejects the statement.

More precisely, the hypothesis testing procedure can be broken down into three steps:

1.

Formulation of a (hypothetical) statement.

The hypothetical statement formed is called the

null hypothesis

, or

H

0

. For example, in testing whether a coin is “fair”, it is tossed 100 times and the number of heads are counted.

H

0

could be

{

The number of heads =

⁢

50

}

. Accompanying the null hypothesis

H

0

is the

alternative hypothesis

H

a

. The statement in

H

a

is the compliment of the statement in

H

0

(the universe is the

sample space

). For example,

H

a

would be

{

The number of heads

≠

50

}

.

2.

Testing of the statement.

This is usually the most mathematical part of the procedure. To test

H

0

, first assume

H

0

is true. Then apply an appropriate

test statistic

using values obtained from the study. There are many test statistics, depending on

H

0

,

H

a

, and the nature of the study.

Based on the distributional forms of these test statistics, four major types of tests are of interest. A

t test

is based on a test statistic that has a

t-distribution

. An

f test

(

http://planetmath.org/fdistribution

) and a

χ

2

test

(

http://planetmath.org/ChiSquaredRandomVariable

) are so named for the same reason. A

z-test

is one that is based on a test statistic having a normal or

Gaussian distribution

.

Before calculating the test statistic, a value of the

significance level

of the test needs to be specified. The significance level, known as

α

, is the

probability

of rejecting

H

0

(or accepting

H

a

) when in fact,

H

0

is true:

α

=

P

(

H

a

∣

H

0

)

.

3.

Deciding whether to accept or reject the statement.

Once the value of the test statistic is obtained, it is used to find a corresponding probability of obtaining such a

statistic

given that

H

0

is true. This probability is called the

p-value

. This

p

-value is then compared

α

, the significance level of the test. If

p

-value

<

α

, then the usual next step is to reject the null hypothesis

H

0

(and

H

a

accepted). Otherwise,

H

0

will be accepted.

When a statement (whether it is null hypothesis or the alternative hypothesis) is accepted, it merely says that, statistically, there is not enough evidence to reject the statement. Acceptance of a hypothetical statement does not prove that the underlying statement is true.

The concept of statistical hypothesis testing can be found in any standard introductory statistics textbooks, as well as numerous internet websites (for example,

http://www.google.com/search?hl=en&lr=&q=hypothesis+testing

click to find the result of a Google search). The purpose of this entry is to give a very brief description of hypothesis testing and to serve as a link reference for other entries.

Title

hypothesis testing

Canonical name

HypothesisTesting

Date of creation

2013-03-22 14:42:12

Last modified on

2013-03-22 14:42:12

Owner

CWoo (3771)

Last modified by

CWoo (3771)

Numerical id

5

Author

CWoo (3771)

Entry type

Definition

Classification

msc 62A01

Related topic

ChiSquaredStatistic

Defines

null hypothesis

Defines

alternative hypothesis

Defines

significance level

Defines

t test

Defines

f test

Defines

chi-squared test

Defines

chi-square test

Defines

χ

2

test

Defines

p-value

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