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multisample comparison

English translation: multiple comparison tests, multiple sample comparison tests, multisample comparison tests

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GLOSSARY ENTRY (DERIVED FROM QUESTION BELOW)
English term or phrase:multisample comparison
English translation:multiple comparison tests, multiple sample comparison tests, multisample comparison tests
Entered by: Jörgen Slet
Options:
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17:28 Nov 8, 2003
English to English translations [PRO]
Science / statistical analysis
English term or phrase: multisample comparison
(I'd appreciate an explanation and any synonyms or at least a related terms as well, if possible.)

The text is about algal growth inhibition test (a toxicity test).


"The No Observed Effect Concentration is determined by a suitable statistical procedure for multisample comparison (e.g. analysis of variance and Dunnett's test), using the individual replicates values of the areas under the growth curves A (see point 2.1) or the specific growth rates u (see point 2.2)."
Jörgen Slet
Estonia
Local time: 14:48
Multisample comparisons
Explanation:
are statisical methods that compare the numerical results from three or more groups/samples(in the sense of collections of results from a group,in this case)....

...to establish whether or not they are significantly different or not (within a certain degree of probability). What is actually compared is one variable/characteristic (or more than one of the variables) in each group, compared with the corresponding variables in the other groups.

An example of the mean value for a variable in a group would be the mean number of eggs in the nest of a group/sample of nesting birds - another variable might be the mean number of years the mother birds have nested.

Anova and Analysis of Variance are the same thing, although there are various sorts of Anova / Analysis of Variance tests to suit different situations

Anova is particularly used to compare mean values for a variable (sometimes more than one) between three or more groups/samples

(as in:
http://www.anselm.edu/homepage/jpitocch/anova/anovapage2.htm...

"ANOVA and Multiple Comparisons

Analysis of Variance - ANOVA

Developed by Fisher while studying agriculture and crop output with different fertlizer treatments, needed test that could evaluate differences between three or more means, developed the F distribution

Why [are there] problems with applying the Student's t test to more than 2 sample/population means....?"


It's normal to use other tests ("t" or "z" depending on size) to compare variables from a pair of groups/samples - anova is used as well for this in some circumstances (as well as for 3 or more groups), but anyway this isn't what is meant by multisample analysis (multisample comparison, etc).

'multiple comparison tests', 'multiple sample comparison tests', and 'multisample comparison [tests]' are names which are sometimes used interchangeably.

'One way Anova' is an Anova test comparing the mean values for one variable between the groups/samples, to see if they are significantly different or not (it compares the groups 'in one way' or aspect).

'Two way Anova' compares mean values for two or more variables between the groups, and so on.

Sometimes an Anova test is made first to establish if there is a significant difference between results for the three or more groups, and this is then followed by a test such as Dunnett to provide additional information (eg: when an Anova test is used to tell you that samples do differ from one another, but doesn't tell you exactly which results are the ones which are different, and more information about this is wanted).

(as in)
http://www.texasoft.com/manual56.htm

"Multiple Comparisons Analysis

When you perform an analysis of variance test, the F-test (main effects) usually is testing for the presence of a difference between factor means. However, if the test is significant, you do not necessarily know which of the several means can be considered significantly different. Some people have tended to perform multiple t-tests to examine the differences between a number of means; however, this approach is incorrect since the p-values associated with multiple tests are no longer appropriate. Multiple comparison tests are designed to allow you to perform differences between all possible pairs of means using an appropriate and controlled significance level.

WINKS allows you to perform four different kinds of multiple comparison tests. They are:

Newman-Keuls (Newman, 1939, Keuls, 1952)
Tukey (Tukey, 1953)
Scheffé (Scheffé, 1953; 1959)
Dunnett (Dunnett, 1955)"


Dunnett (Dunnett, 1955)
Dunnett's test is a specialized multiple comparison test that allows you to compare a single control group to all other groups. [others eg: Tukey (Tukey, 1953), Scheffé (Scheffé, 1953; 1959) ]
The test you use depends, in part, on your discipline. Some areas of research prefer one multiple comparison test over another. You should consult your literature to see which of these tests is most often used.
http://www.texasoft.com/manual56.htm


--------------------------------------------------
Note added at 20 hrs 32 mins (2003-11-09 14:00:53 GMT)
--------------------------------------------------

typo.error:
- when I said: \"What is actually compared is one variable/characteristic (or more than one of the variables) in each group, compared with the corresponding variables in the other groups.\"

I meant: \" What is actually compared is the value for one variable/characteristic (or more than one of the variables) in each group, compared with the corresponding values in the other groups.\"


--------------------------------------------------
Note added at 21 hrs 4 mins (2003-11-09 14:32:58 GMT)
--------------------------------------------------

Why do we need Anova (and Multisample comparison) ?

There are methods of comparing the mean values of two samples. Sometimes however biologists wish to compare the means of more than two samples. Suppose, for example, we have the length measurements from samples of three races of a species each of which lives on different islands A, B and C. It is possible to compare mean lengths [to see if there are significant differences] by z-tests or,if the samples are small, by t-tests. We would need to perform the test three times to compare A-B, A-C and B-C. With the help of a calculator the task is not too daunting. Let us imagine instead that we wish to compare the means of seven samples. In this event no less than 21 z-tests are required to compare all possible pairs of means. Even if the analyst has sufficient patience to work through this cumbersome treatment, there is an underlying statistical objection to doing so.
For example, if the \"P\"=0.05 (probability level is set at 5%) level of significance, a wrong conclusion will be drawn on average every 20 tests. In 21 z-tests there is a good chance of at least one false conclusion. Anova overcomes these problems by allowing comparisons to be made between any number of sample mean values, all in a single test.
(With thanks to \"Practical Statistics For Field Biology\" Jim Fowler, Lou Cohen & Phil Jarvis - pub. Wiley 1998
Chapter 17 Analysis of Variance - Anova)
Selected response from:

DGK T-I
United Kingdom
Local time: 12:48
Grading comment
Most thorough explanation, thank you !
4 KudoZ points were awarded for this answer

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Summary of answers provided
3 +2comparing multiple samples (statistical term)RHELLER
4Multisample comparisons
DGK T-I
1see de link
Claudia Serban


  

Answers


24 mins   confidence: Answerer confidence 1/5Answerer confidence 1/5
see de link


Explanation:
Two-sample vs. Anova

Anova is an extension of the t-test. If you are testing for difference in means for 2 groups you would use a t-test (or its nonparametric equivalent, the Mann-Whitney U-test). For more than 2 groups, i.e., a multisample comparison, you would use a 1-way Anova.



    Reference: http://www.epa.gov/bioindicators/primer/tableall.html
Claudia Serban
Local time: 14:48
Native speaker of: Native in RomanianRomanian
Login to enter a peer comment (or grade)

22 mins   confidence: Answerer confidence 3/5Answerer confidence 3/5 peer agreement (net): +2
comparing multiple samples (statistical term)


Explanation:
Multiple-Sample Comparison. Analysis Summary Summary Statistics ANOVA Table Table of Means Multiple Range Tests Variance Check Kruskal-Wallis Test. ...
www.statlets.com/standard_edition.htm

X. X. Two-Sample Comparison, X. X. X. X. Paired-Sample Comparison, X. X. X. X. Multiple-Sample Comparison, X. X. X. X. Comparison of Proportions, X. X. X. X. Comparison of Rates,
X. X. ...
www.statlets.com/statgraphics.htm

--------------------------------------------------
Note added at 25 mins (2003-11-08 17:54:21 GMT)
--------------------------------------------------

statlets.com is linked to resources for different countries

--------------------------------------------------
Note added at 27 mins (2003-11-08 17:56:14 GMT)
--------------------------------------------------

Statistical Analysis

All results are given as means ± SE. Mean values were compared using analysis of variance (ANOVA). The Newman-Keuls multiple-sample comparison test was used to evaluate any differences in results. p Values less than 0.05 were considered statistically significant.

http://ajrccm.atsjournals.org/cgi/content/full/159/2/544

RHELLER
United States
Local time: 05:48
Native speaker of: Native in EnglishEnglish
PRO pts in pair: 1252

Peer comments on this answer (and responses from the answerer)
agree  Refugio: or comparison of multiple samples
12 hrs

agree  chopra_2002
17 hrs
Login to enter a peer comment (or grade)

20 hrs   confidence: Answerer confidence 4/5Answerer confidence 4/5
Multisample comparisons


Explanation:
are statisical methods that compare the numerical results from three or more groups/samples(in the sense of collections of results from a group,in this case)....

...to establish whether or not they are significantly different or not (within a certain degree of probability). What is actually compared is one variable/characteristic (or more than one of the variables) in each group, compared with the corresponding variables in the other groups.

An example of the mean value for a variable in a group would be the mean number of eggs in the nest of a group/sample of nesting birds - another variable might be the mean number of years the mother birds have nested.

Anova and Analysis of Variance are the same thing, although there are various sorts of Anova / Analysis of Variance tests to suit different situations

Anova is particularly used to compare mean values for a variable (sometimes more than one) between three or more groups/samples

(as in:
http://www.anselm.edu/homepage/jpitocch/anova/anovapage2.htm...

"ANOVA and Multiple Comparisons

Analysis of Variance - ANOVA

Developed by Fisher while studying agriculture and crop output with different fertlizer treatments, needed test that could evaluate differences between three or more means, developed the F distribution

Why [are there] problems with applying the Student's t test to more than 2 sample/population means....?"


It's normal to use other tests ("t" or "z" depending on size) to compare variables from a pair of groups/samples - anova is used as well for this in some circumstances (as well as for 3 or more groups), but anyway this isn't what is meant by multisample analysis (multisample comparison, etc).

'multiple comparison tests', 'multiple sample comparison tests', and 'multisample comparison [tests]' are names which are sometimes used interchangeably.

'One way Anova' is an Anova test comparing the mean values for one variable between the groups/samples, to see if they are significantly different or not (it compares the groups 'in one way' or aspect).

'Two way Anova' compares mean values for two or more variables between the groups, and so on.

Sometimes an Anova test is made first to establish if there is a significant difference between results for the three or more groups, and this is then followed by a test such as Dunnett to provide additional information (eg: when an Anova test is used to tell you that samples do differ from one another, but doesn't tell you exactly which results are the ones which are different, and more information about this is wanted).

(as in)
http://www.texasoft.com/manual56.htm

"Multiple Comparisons Analysis

When you perform an analysis of variance test, the F-test (main effects) usually is testing for the presence of a difference between factor means. However, if the test is significant, you do not necessarily know which of the several means can be considered significantly different. Some people have tended to perform multiple t-tests to examine the differences between a number of means; however, this approach is incorrect since the p-values associated with multiple tests are no longer appropriate. Multiple comparison tests are designed to allow you to perform differences between all possible pairs of means using an appropriate and controlled significance level.

WINKS allows you to perform four different kinds of multiple comparison tests. They are:

Newman-Keuls (Newman, 1939, Keuls, 1952)
Tukey (Tukey, 1953)
Scheffé (Scheffé, 1953; 1959)
Dunnett (Dunnett, 1955)"


Dunnett (Dunnett, 1955)
Dunnett's test is a specialized multiple comparison test that allows you to compare a single control group to all other groups. [others eg: Tukey (Tukey, 1953), Scheffé (Scheffé, 1953; 1959) ]
The test you use depends, in part, on your discipline. Some areas of research prefer one multiple comparison test over another. You should consult your literature to see which of these tests is most often used.
http://www.texasoft.com/manual56.htm


--------------------------------------------------
Note added at 20 hrs 32 mins (2003-11-09 14:00:53 GMT)
--------------------------------------------------

typo.error:
- when I said: \"What is actually compared is one variable/characteristic (or more than one of the variables) in each group, compared with the corresponding variables in the other groups.\"

I meant: \" What is actually compared is the value for one variable/characteristic (or more than one of the variables) in each group, compared with the corresponding values in the other groups.\"


--------------------------------------------------
Note added at 21 hrs 4 mins (2003-11-09 14:32:58 GMT)
--------------------------------------------------

Why do we need Anova (and Multisample comparison) ?

There are methods of comparing the mean values of two samples. Sometimes however biologists wish to compare the means of more than two samples. Suppose, for example, we have the length measurements from samples of three races of a species each of which lives on different islands A, B and C. It is possible to compare mean lengths [to see if there are significant differences] by z-tests or,if the samples are small, by t-tests. We would need to perform the test three times to compare A-B, A-C and B-C. With the help of a calculator the task is not too daunting. Let us imagine instead that we wish to compare the means of seven samples. In this event no less than 21 z-tests are required to compare all possible pairs of means. Even if the analyst has sufficient patience to work through this cumbersome treatment, there is an underlying statistical objection to doing so.
For example, if the \"P\"=0.05 (probability level is set at 5%) level of significance, a wrong conclusion will be drawn on average every 20 tests. In 21 z-tests there is a good chance of at least one false conclusion. Anova overcomes these problems by allowing comparisons to be made between any number of sample mean values, all in a single test.
(With thanks to \"Practical Statistics For Field Biology\" Jim Fowler, Lou Cohen & Phil Jarvis - pub. Wiley 1998
Chapter 17 Analysis of Variance - Anova)


    Reference: http://www.anselm.edu/homepage/jpitocch/anova/anovapage2.htm...
    Reference: http://www.texasoft.com/manual56.htm/
DGK T-I
United Kingdom
Local time: 12:48
PRO pts in pair: 401
Grading comment
Most thorough explanation, thank you !
Login to enter a peer comment (or grade)




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