horng99

02-10-1999, 10:28 AM

Dear biomch-lers:

I greatly appreciate all who responded on my request on t -tests.

Thanks again. Below is a list of responses:

1

The Student's t-test is a statistical test done on the data once it is

collected to determine if there is a statistical "significant"

difference between groups. The subject matter is irrelevant. As for

the paired and unpaired t-test that refers to the type of data groups

that you have. Eg Paired means that you have paired data like left and

right pedicle purchase from the SAME specimen, another example is if

you are testing the wear pattern on car tires of A and B on the car,

since both are tested on the same car with the SAME testing

parameters, you have paired samples...therefore a regular test is not

sufficient.

2

Two-tailed vs one-tailed t-tests refers to the normal distribution

curve of what you are interested in determining the probability of

making a Type one (alpha) error (when you reject your null hypothesis

when in fact it is true). Given by the P-Value. Whether you use a one

or two tailed depends on your testing hypothesis eg does not equal to

would be a 2-tailed and greater than or less than would be a

one-tailed test.

This is more or less of the basics-you will find all this in a

introductory undergrad statistics text.

3

t-tests (paired and unpaired) are statistical tests used to assess

significance between your variables - they are used by all disciplines

for all types of data. If you have not had any exposure to statistics

you should consult with someone who has. There are software packages

available (SPSS, SAS, BMDP) that are used to assess statistical

differences between any type of data.

t-tests are for testing for significant differences between groups. A

t-test can also be used for a single group to test for a significant

difference from zero, or any other value.

4

The correct usage of a t-test is dependent upon the data collection,

and the research question. There are basically two types: 1) paired

and 2) unpaired, which you mentioned in your post. The paired t-test

implies that the measures were taken on the same individuals on two

different occasions, or that there is some other inherent

dependency among the groups. For example, testing subjects with the

same screw would lead to a paired test. The unpaired t-test would be

used when the groups are unrelated. For example, when you speak of

testing the BMD between unicortical and bicortical screws, it would

appear that an unpaired t-test would be the most appropriate. I am

not sure what you mean by differentiating between success and

failure of unicortical groups. However, if you were testing, say, EMG

activity in unicortical screws the correct application would be a

paired t-test.

5

In a t-test it is possible to test for both one-tailed and two-tailed

significance.

If you have no prior knowledge of the type of outcome you expect, than

a

two-tailed test would be appropriate. If you have an inkling as to a

group

increasing or decreasing its value, than you may use a one-tailed test.

Every statistical software package should have each of the t-test

options

available. A very good text on Inferential Statistics is by Glass and

Hopkins.

I do not have the text in my office at the moment, so I can't give you

the

complete reference. However, it is extremely easy to read and very

intuitive.

6

I read your e-mail on the BIOMCH-L listserver about t-tests. I am not

certain what you were asking, but I think you are looking for a

software package which performs t-tests. If you have Microsoft Excel,

it can easily and quickly do the t-tests you mentioned. In Excel,

after you have entered the data, from the "Tools" menu select "Data

Analysis." If you do not see "Data Analysis" in the menu, select

"Add-ins" and check the "Analysis ToolPak" button. The "Data

Analysis" option should then appear in the "Tools" menu. In the "Data

Analysis" dialog box, scroll down to t-tests. You can select from

Paired, Unpaired assuming equal variances or Unpaired assuming

unequal variances. The last one (assuming unequal variances) is used

if you have two groups of differing sizes (unequal N). The rest

should be self-explanatory if you have used Excel before, or you can

use the Help file to determine what you need to enter.

7

All introductory stats texts describe these tests. The paired t-tests

should be used for repeated measures (before/after) or when scores are

paired for some relevant variable (say two screws into the same bone).

Concerning unpaired t-tests a test for equal and unequal variances

should be made then the appropriate formula should be used to compute

the t value.

In my opinion one should always use a two-tailed test. This tests

whether

the two measures are different or not. If the relevant variable falls

in the

correct tail then you report that it is better, stronger, faster ...

etc.

Many people use a one-tailed test but never consider what they would

do if the

results end up in the wrong tail (i.e., worse, weaker, slower ...).

If you

can honestly say that it doesn't matter than use a one-tailed test.

Most

people, however, will consider that the results are significantly

reversed and

therefore a two-tailed test is necessary.

__________________________________________________ _______

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I greatly appreciate all who responded on my request on t -tests.

Thanks again. Below is a list of responses:

1

The Student's t-test is a statistical test done on the data once it is

collected to determine if there is a statistical "significant"

difference between groups. The subject matter is irrelevant. As for

the paired and unpaired t-test that refers to the type of data groups

that you have. Eg Paired means that you have paired data like left and

right pedicle purchase from the SAME specimen, another example is if

you are testing the wear pattern on car tires of A and B on the car,

since both are tested on the same car with the SAME testing

parameters, you have paired samples...therefore a regular test is not

sufficient.

2

Two-tailed vs one-tailed t-tests refers to the normal distribution

curve of what you are interested in determining the probability of

making a Type one (alpha) error (when you reject your null hypothesis

when in fact it is true). Given by the P-Value. Whether you use a one

or two tailed depends on your testing hypothesis eg does not equal to

would be a 2-tailed and greater than or less than would be a

one-tailed test.

This is more or less of the basics-you will find all this in a

introductory undergrad statistics text.

3

t-tests (paired and unpaired) are statistical tests used to assess

significance between your variables - they are used by all disciplines

for all types of data. If you have not had any exposure to statistics

you should consult with someone who has. There are software packages

available (SPSS, SAS, BMDP) that are used to assess statistical

differences between any type of data.

t-tests are for testing for significant differences between groups. A

t-test can also be used for a single group to test for a significant

difference from zero, or any other value.

4

The correct usage of a t-test is dependent upon the data collection,

and the research question. There are basically two types: 1) paired

and 2) unpaired, which you mentioned in your post. The paired t-test

implies that the measures were taken on the same individuals on two

different occasions, or that there is some other inherent

dependency among the groups. For example, testing subjects with the

same screw would lead to a paired test. The unpaired t-test would be

used when the groups are unrelated. For example, when you speak of

testing the BMD between unicortical and bicortical screws, it would

appear that an unpaired t-test would be the most appropriate. I am

not sure what you mean by differentiating between success and

failure of unicortical groups. However, if you were testing, say, EMG

activity in unicortical screws the correct application would be a

paired t-test.

5

In a t-test it is possible to test for both one-tailed and two-tailed

significance.

If you have no prior knowledge of the type of outcome you expect, than

a

two-tailed test would be appropriate. If you have an inkling as to a

group

increasing or decreasing its value, than you may use a one-tailed test.

Every statistical software package should have each of the t-test

options

available. A very good text on Inferential Statistics is by Glass and

Hopkins.

I do not have the text in my office at the moment, so I can't give you

the

complete reference. However, it is extremely easy to read and very

intuitive.

6

I read your e-mail on the BIOMCH-L listserver about t-tests. I am not

certain what you were asking, but I think you are looking for a

software package which performs t-tests. If you have Microsoft Excel,

it can easily and quickly do the t-tests you mentioned. In Excel,

after you have entered the data, from the "Tools" menu select "Data

Analysis." If you do not see "Data Analysis" in the menu, select

"Add-ins" and check the "Analysis ToolPak" button. The "Data

Analysis" option should then appear in the "Tools" menu. In the "Data

Analysis" dialog box, scroll down to t-tests. You can select from

Paired, Unpaired assuming equal variances or Unpaired assuming

unequal variances. The last one (assuming unequal variances) is used

if you have two groups of differing sizes (unequal N). The rest

should be self-explanatory if you have used Excel before, or you can

use the Help file to determine what you need to enter.

7

All introductory stats texts describe these tests. The paired t-tests

should be used for repeated measures (before/after) or when scores are

paired for some relevant variable (say two screws into the same bone).

Concerning unpaired t-tests a test for equal and unequal variances

should be made then the appropriate formula should be used to compute

the t value.

In my opinion one should always use a two-tailed test. This tests

whether

the two measures are different or not. If the relevant variable falls

in the

correct tail then you report that it is better, stronger, faster ...

etc.

Many people use a one-tailed test but never consider what they would

do if the

results end up in the wrong tail (i.e., worse, weaker, slower ...).

If you

can honestly say that it doesn't matter than use a one-tailed test.

Most

people, however, will consider that the results are significantly

reversed and

therefore a two-tailed test is necessary.

__________________________________________________ _______

DO YOU YAHOO!?

Get your free @yahoo.com address at http://mail.yahoo.com

---------------------------------------------------------------

To unsubscribe send SIGNOFF BIOMCH-L to LISTSERV@nic.surfnet.nl

For information and archives: http://isb.ri.ccf.org/biomch-l

---------------------------------------------------------------