Difference between revisions of "Student test"
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+ | '' $ t $- | ||
+ | test'' | ||
A [[Significance test|significance test]] for the mean value of a [[Normal distribution|normal distribution]]. | A [[Significance test|significance test]] for the mean value of a [[Normal distribution|normal distribution]]. | ||
==The single-sample Student test.== | ==The single-sample Student test.== | ||
− | Let the independent random variables | + | Let the independent random variables $ X _ {1} \dots X _ {n} $ |
+ | be subject to the normal law $ N _ {1} ( a, \sigma ^ {2} ) $, | ||
+ | the parameters $ a $ | ||
+ | and $ \sigma ^ {2} $ | ||
+ | of which are unknown, and let a [[Simple hypothesis|simple hypothesis]] $ H _ {0} $: | ||
+ | $ a = a _ {0} $ | ||
+ | be tested against the composite alternative $ H _ {1} $: | ||
+ | $ a \neq a _ {0} $. | ||
+ | In solving this problem, a Student test is used, based on the statistic | ||
− | + | $$ | |
+ | t _ {n-1} = \sqrt n | ||
+ | \frac{\overline{X}\; - a _ {0} }{s} | ||
+ | , | ||
+ | $$ | ||
where | where | ||
− | + | $$ | |
+ | \overline{X}\; = | ||
+ | \frac{1}{n} | ||
+ | \sum _ { i= 1} ^ { n } X _ {i} \ \textrm{ and } \ \ | ||
+ | s ^ {2} = | ||
+ | \frac{1}{n-1} | ||
+ | \sum _ { i= 1} ^ { n } ( X _ {i} - \overline{X}\; ) ^ {2} | ||
+ | $$ | ||
− | are estimators of the parameters | + | are estimators of the parameters $ a $ |
+ | and $ \sigma ^ {2} $, | ||
+ | calculated with respect to the sample $ X _ {1} \dots X _ {n} $. | ||
+ | When $ H _ {0} $ | ||
+ | is correct, the statistic $ t _ {n-1} $ | ||
+ | is subject to the [[Student distribution]] with $ f = n- 1 $ | ||
+ | degrees of freedom, i.e. | ||
− | < | + | $$ |
+ | {\mathsf P} \{ | t _ {n-1} | < t \mid H _ {0} \} = \ | ||
+ | 2S _ {n-1} ( t) - 1,\ \ | ||
+ | t > 0, | ||
+ | $$ | ||
− | where | + | where $ S _ {f} ( t) $ |
+ | is the Student distribution function with $ f $ | ||
+ | degrees of freedom. According to the single-sample Student test with significance level $ \alpha $, | ||
+ | $ 0 < \alpha < 0.5 $, | ||
+ | the hypothesis $ H _ {0} $ | ||
+ | must be accepted if | ||
− | + | $$ | |
+ | \left | \sqrt n | ||
+ | \frac{\overline{X}\; - a _ {0} }{s} | ||
+ | \right | < t _ {n-1} \left ( 1 - | ||
− | + | \frac \alpha {2} | |
+ | \right ) , | ||
+ | $$ | ||
− | + | where $ t _ {n-1} ( 1- \alpha /2) $ | |
+ | is the [[quantile]] of level $ 1- \alpha /2 $ | ||
+ | of the Student distribution with $ f= n- 1 $ | ||
+ | degrees of freedom, i.e. $ t _ {n-1} ( 1- \alpha /2) $ | ||
+ | is the solution of the equation $ S _ {n-1} ( t) = 1- \alpha /2 $. | ||
+ | On the other hand, if | ||
− | then, according to the Student test of level | + | $$ |
+ | \left | \sqrt n | ||
+ | \frac{\overline{X}\; - a _ {0} }{s} | ||
+ | \right | \geq t _ {n-1} \left ( 1 - | ||
+ | |||
+ | \frac \alpha {2} | ||
+ | \right ) , | ||
+ | $$ | ||
+ | |||
+ | then, according to the Student test of level $ \alpha $, | ||
+ | the tested hypothesis $ H _ {0} $: | ||
+ | $ a = a _ {0} $ | ||
+ | has to be rejected, and the alternative hypothesis $ H _ {1} $: | ||
+ | $ a \neq a _ {0} $ | ||
+ | has to be accepted. | ||
==The two-sample Student test.== | ==The two-sample Student test.== | ||
− | Let | + | Let $ X _ {1} \dots X _ {n} $ |
+ | and $ Y _ {1} \dots Y _ {m} $ | ||
+ | be mutually independent normally-distributed random variables with the same unknown variance $ \sigma ^ {2} $, | ||
+ | and let | ||
+ | |||
+ | $$ | ||
+ | {\mathsf E} X _ {1} = \dots = {\mathsf E} X _ {n} = a _ {1} , | ||
+ | $$ | ||
− | + | $$ | |
+ | {\mathsf E} Y _ {1} = \dots = {\mathsf E} Y _ {m} = a _ {2} , | ||
+ | $$ | ||
− | + | where the parameters $ a _ {1} $ | |
+ | and $ a _ {2} $ | ||
+ | are also unknown (it is often said that there are two independent normal samples). Moreover, let the hypothesis $ H _ {0} $: | ||
+ | $ a _ {1} = a _ {2} $ | ||
+ | be tested against the alternative $ H _ {1} $: | ||
+ | $ a _ {1} \neq a _ {2} $. | ||
+ | In this instance, both hypotheses are composite. Using the observations $ X _ {1} \dots X _ {n} $ | ||
+ | and $ Y _ {1} \dots Y _ {m} $ | ||
+ | it is possible to calculate the estimators | ||
− | + | $$ | |
+ | \overline{X}\; = | ||
+ | \frac{1}{n} | ||
+ | \sum _ { i=1 } ^ { n } X _ {i} \ \textrm{ and } \ \ | ||
+ | \overline{Y}\; = | ||
+ | \frac{1}{m} | ||
+ | \sum _ { j= 1} ^ { m } Y _ {j} $$ | ||
− | + | for the unknown mathematical expectations $ a _ {1} $ | |
+ | and $ a _ {2} $, | ||
+ | as well as the estimators | ||
− | + | $$ | |
+ | s _ {1} ^ {2} = | ||
+ | \frac{1}{n-1} | ||
+ | \sum _ { i= 1} ^ { n } ( X _ {i} - \overline{X}\; ) ^ {2} | ||
+ | \ \textrm{ and } \ \ | ||
+ | s _ {2} ^ {2} = | ||
+ | \frac{1}{m-1} | ||
+ | \sum _ { j= 1} ^ { m } ( Y _ {j} - \overline{Y}\; ) ^ {2} | ||
+ | $$ | ||
− | + | for the unknown variance $ \sigma ^ {2} $. | |
+ | Moreover, let | ||
− | + | $$ | |
+ | s ^ {2} = | ||
+ | \frac{1}{n+m- 2} [( n- 1) s _ {1} ^ {2} + ( m- 1) s _ {2} ^ {2} ]. | ||
+ | $$ | ||
− | + | Then, when $ H _ {0} $ | |
+ | is correct, the statistic | ||
− | + | $$ | |
+ | t _ {n+} m- 2 = | ||
+ | \frac{\overline{X}\; - \overline{Y}\; }{s \sqrt 1/n+ 1/m } | ||
− | + | $$ | |
− | is subject to the Student distribution with | + | is subject to the Student distribution with $ f = n+ m- 2 $ |
+ | degrees of freedom. This fact forms the basis of the two-sample Student test for testing $ H _ {0} $ | ||
+ | against $ H _ {1} $. | ||
+ | According to the two-sample Student test of level $ \alpha $, | ||
+ | $ 0 < \alpha < 0.5 $, | ||
+ | the hypothesis $ H _ {0} $ | ||
+ | is accepted if | ||
− | < | + | $$ |
+ | | t _ {n+ m- 2} | < t _ {n+m- 2} \left ( 1 - | ||
+ | \frac \alpha {2} | ||
+ | \right ) , | ||
+ | $$ | ||
− | where | + | where $ t _ {n+m- 2} ( 1- \alpha /2) $ |
+ | is the quantile of level $ 1- \alpha /2 $ | ||
+ | of the Student distribution with $ f= n+ m- 2 $ | ||
+ | degrees of freedom. If | ||
− | + | $$ | |
+ | | t _ {n+} m- 2 | \geq t _ {n+m- 2} \left ( 1- | ||
+ | \frac \alpha {2} | ||
+ | \right ) , | ||
+ | $$ | ||
− | then, according to the Student test of level | + | then, according to the Student test of level $ \alpha $, |
+ | the hypothesis $ H _ {0} $ | ||
+ | is rejected in favour of $ H _ {1} $. | ||
====References==== | ====References==== | ||
<table><TR><TD valign="top">[1]</TD> <TD valign="top"> H. Cramér, "Mathematical methods of statistics" , Princeton Univ. Press (1946)</TD></TR><TR><TD valign="top">[2]</TD> <TD valign="top"> S.S. Wilks, "Mathematical statistics" , Wiley (1962)</TD></TR><TR><TD valign="top">[3]</TD> <TD valign="top"> N.V. Smirnov, I.V. Dunin-Barkovskii, "Mathematische Statistik in der Technik" , Deutsch. Verlag Wissenschaft. (1969) (Translated from Russian)</TD></TR><TR><TD valign="top">[4]</TD> <TD valign="top"> L.N. Bol'shev, N.V. Smirnov, "Tables of mathematical statistics" , ''Libr. math. tables'' , '''46''' , Nauka (1983) (In Russian) (Processed by L.S. Bark and E.S. Kedrova)</TD></TR><TR><TD valign="top">[5]</TD> <TD valign="top"> Yu.V. Linnik, "Methoden der kleinsten Quadraten in moderner Darstellung" , Deutsch. Verlag Wissenschaft. (1961) (Translated from Russian)</TD></TR></table> | <table><TR><TD valign="top">[1]</TD> <TD valign="top"> H. Cramér, "Mathematical methods of statistics" , Princeton Univ. Press (1946)</TD></TR><TR><TD valign="top">[2]</TD> <TD valign="top"> S.S. Wilks, "Mathematical statistics" , Wiley (1962)</TD></TR><TR><TD valign="top">[3]</TD> <TD valign="top"> N.V. Smirnov, I.V. Dunin-Barkovskii, "Mathematische Statistik in der Technik" , Deutsch. Verlag Wissenschaft. (1969) (Translated from Russian)</TD></TR><TR><TD valign="top">[4]</TD> <TD valign="top"> L.N. Bol'shev, N.V. Smirnov, "Tables of mathematical statistics" , ''Libr. math. tables'' , '''46''' , Nauka (1983) (In Russian) (Processed by L.S. Bark and E.S. Kedrova)</TD></TR><TR><TD valign="top">[5]</TD> <TD valign="top"> Yu.V. Linnik, "Methoden der kleinsten Quadraten in moderner Darstellung" , Deutsch. Verlag Wissenschaft. (1961) (Translated from Russian)</TD></TR></table> |
Latest revision as of 20:10, 10 January 2024
$ t $-
test
A significance test for the mean value of a normal distribution.
The single-sample Student test.
Let the independent random variables $ X _ {1} \dots X _ {n} $ be subject to the normal law $ N _ {1} ( a, \sigma ^ {2} ) $, the parameters $ a $ and $ \sigma ^ {2} $ of which are unknown, and let a simple hypothesis $ H _ {0} $: $ a = a _ {0} $ be tested against the composite alternative $ H _ {1} $: $ a \neq a _ {0} $. In solving this problem, a Student test is used, based on the statistic
$$ t _ {n-1} = \sqrt n \frac{\overline{X}\; - a _ {0} }{s} , $$
where
$$ \overline{X}\; = \frac{1}{n} \sum _ { i= 1} ^ { n } X _ {i} \ \textrm{ and } \ \ s ^ {2} = \frac{1}{n-1} \sum _ { i= 1} ^ { n } ( X _ {i} - \overline{X}\; ) ^ {2} $$
are estimators of the parameters $ a $ and $ \sigma ^ {2} $, calculated with respect to the sample $ X _ {1} \dots X _ {n} $. When $ H _ {0} $ is correct, the statistic $ t _ {n-1} $ is subject to the Student distribution with $ f = n- 1 $ degrees of freedom, i.e.
$$ {\mathsf P} \{ | t _ {n-1} | < t \mid H _ {0} \} = \ 2S _ {n-1} ( t) - 1,\ \ t > 0, $$
where $ S _ {f} ( t) $ is the Student distribution function with $ f $ degrees of freedom. According to the single-sample Student test with significance level $ \alpha $, $ 0 < \alpha < 0.5 $, the hypothesis $ H _ {0} $ must be accepted if
$$ \left | \sqrt n \frac{\overline{X}\; - a _ {0} }{s} \right | < t _ {n-1} \left ( 1 - \frac \alpha {2} \right ) , $$
where $ t _ {n-1} ( 1- \alpha /2) $ is the quantile of level $ 1- \alpha /2 $ of the Student distribution with $ f= n- 1 $ degrees of freedom, i.e. $ t _ {n-1} ( 1- \alpha /2) $ is the solution of the equation $ S _ {n-1} ( t) = 1- \alpha /2 $. On the other hand, if
$$ \left | \sqrt n \frac{\overline{X}\; - a _ {0} }{s} \right | \geq t _ {n-1} \left ( 1 - \frac \alpha {2} \right ) , $$
then, according to the Student test of level $ \alpha $, the tested hypothesis $ H _ {0} $: $ a = a _ {0} $ has to be rejected, and the alternative hypothesis $ H _ {1} $: $ a \neq a _ {0} $ has to be accepted.
The two-sample Student test.
Let $ X _ {1} \dots X _ {n} $ and $ Y _ {1} \dots Y _ {m} $ be mutually independent normally-distributed random variables with the same unknown variance $ \sigma ^ {2} $, and let
$$ {\mathsf E} X _ {1} = \dots = {\mathsf E} X _ {n} = a _ {1} , $$
$$ {\mathsf E} Y _ {1} = \dots = {\mathsf E} Y _ {m} = a _ {2} , $$
where the parameters $ a _ {1} $ and $ a _ {2} $ are also unknown (it is often said that there are two independent normal samples). Moreover, let the hypothesis $ H _ {0} $: $ a _ {1} = a _ {2} $ be tested against the alternative $ H _ {1} $: $ a _ {1} \neq a _ {2} $. In this instance, both hypotheses are composite. Using the observations $ X _ {1} \dots X _ {n} $ and $ Y _ {1} \dots Y _ {m} $ it is possible to calculate the estimators
$$ \overline{X}\; = \frac{1}{n} \sum _ { i=1 } ^ { n } X _ {i} \ \textrm{ and } \ \ \overline{Y}\; = \frac{1}{m} \sum _ { j= 1} ^ { m } Y _ {j} $$
for the unknown mathematical expectations $ a _ {1} $ and $ a _ {2} $, as well as the estimators
$$ s _ {1} ^ {2} = \frac{1}{n-1} \sum _ { i= 1} ^ { n } ( X _ {i} - \overline{X}\; ) ^ {2} \ \textrm{ and } \ \ s _ {2} ^ {2} = \frac{1}{m-1} \sum _ { j= 1} ^ { m } ( Y _ {j} - \overline{Y}\; ) ^ {2} $$
for the unknown variance $ \sigma ^ {2} $. Moreover, let
$$ s ^ {2} = \frac{1}{n+m- 2} [( n- 1) s _ {1} ^ {2} + ( m- 1) s _ {2} ^ {2} ]. $$
Then, when $ H _ {0} $ is correct, the statistic
$$ t _ {n+} m- 2 = \frac{\overline{X}\; - \overline{Y}\; }{s \sqrt 1/n+ 1/m } $$
is subject to the Student distribution with $ f = n+ m- 2 $ degrees of freedom. This fact forms the basis of the two-sample Student test for testing $ H _ {0} $ against $ H _ {1} $. According to the two-sample Student test of level $ \alpha $, $ 0 < \alpha < 0.5 $, the hypothesis $ H _ {0} $ is accepted if
$$ | t _ {n+ m- 2} | < t _ {n+m- 2} \left ( 1 - \frac \alpha {2} \right ) , $$
where $ t _ {n+m- 2} ( 1- \alpha /2) $ is the quantile of level $ 1- \alpha /2 $ of the Student distribution with $ f= n+ m- 2 $ degrees of freedom. If
$$ | t _ {n+} m- 2 | \geq t _ {n+m- 2} \left ( 1- \frac \alpha {2} \right ) , $$
then, according to the Student test of level $ \alpha $, the hypothesis $ H _ {0} $ is rejected in favour of $ H _ {1} $.
References
[1] | H. Cramér, "Mathematical methods of statistics" , Princeton Univ. Press (1946) |
[2] | S.S. Wilks, "Mathematical statistics" , Wiley (1962) |
[3] | N.V. Smirnov, I.V. Dunin-Barkovskii, "Mathematische Statistik in der Technik" , Deutsch. Verlag Wissenschaft. (1969) (Translated from Russian) |
[4] | L.N. Bol'shev, N.V. Smirnov, "Tables of mathematical statistics" , Libr. math. tables , 46 , Nauka (1983) (In Russian) (Processed by L.S. Bark and E.S. Kedrova) |
[5] | Yu.V. Linnik, "Methoden der kleinsten Quadraten in moderner Darstellung" , Deutsch. Verlag Wissenschaft. (1961) (Translated from Russian) |
Student test. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Student_test&oldid=17068