The t-distribution table - how to use

The t-distribution table - how to use

t-distribution table

How to use the t-table

An experiment has 2 groups of subjects, 6 subjects in each group, 12 subjects total. Group 1 received a placebo (an inert substance). Group 2 received a drug. The temperature of each subject was recorded. We have 12 scores, 6 scores in each group.

Step 1. We calculate the means, mean 1 and mean 2.
Step 2. We calculate the degrees of freedom, df, is 10. How do we calculate df? df=total number of scores minus the number of means. In this case 12-2=10.
Step 3. We run the t-test for independent groups and, suppose, we find that t=4.52. We call this the calculated t.
Step 4. Now we go to the t-table and enter the left column at df 10
We now slide our finger to the right as far as column with the heading 95% two sided (also called two tailed). The number we see here is 2.228. We call this the required t.
Step 5. We now compare our calculated t to the required t, i.e.the t in the table. Calculated t=4.53. Required t=2.22.
The calculated t is greater than the required t, i.e. the table t.

We conclude that we have significance. This means that the difference between mean 1 and mean 2 is significant (loosely speaking that it is real and not a chance event). We report this as follows:
"p<0.05" . P less than point o five.

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What is one-tail two-tail? How do I choose?

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sigma's picture

One-tail two-tail refers to the tails, the noses of the normal curve. In determining the p value, you may wish to consider weather what you measured in your experiment could vary up and down (two-tail) or only up or only down (one-tail). Examples: If in your experiment you are measuring height in teenagers as a result of a diet, you have a case of one-tail because you do not expect tanagers to grow shorter. If, however, you are measuring body weight, you have a case of two-tail, because tanagers may lose or gain body weight as a result of a diet.

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Look 95% two side at more than df 120. It is 1.96 .get it?

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I don't get it,

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p 0.05. Wow

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So, t distribution is adjusted for small samples?

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yes, for df, i.e. n

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