Excel t-test?

Question

What difference does performing a t-test assuming unequal variance do over performing one with equal variance?

that doesn't answer the question, u've just copy and pasted summat

Answer 1

We use this test for comparing the means of two treatments, even if they have different numbers of replicates. In simple terms, the t-test compares the actual difference between two means in relation to the variation in the data (expressed as the standard deviation of the difference between the means).

Procedure

First, we will see how to do this test using "pencil and paper" (with a calculator to help with the calculations). Then we can see how the same test can be done in a statistical package (Microsoft 'Excel')

1. List the data for treatment 1.

2. List the data for treatment 2.

2. Record the number (n) of replicates for each treatment (the number of replicates for treatment 1 being termed n1 and the number for treatment 2 being termed n2)

3. Calculate mean of each treatment (1 and 2).

4. Calculate s 2 for each treatment; call these s 12 and s 22

5. Calculate the variance of the difference between the two means (sd2) as follows

6. Calculate sd (the square root of sd2)

7. Calculate the t value as follows:

(when doing this, transpose 1 and 2 if 2 > 1 so that you always get a positive value)

8. Enter the t-table at (n1 + n2 -2) degrees of freedom; choose the level of significance required (normally p = 0.05) and read the tabulated t value.

9. If the calculated t value exceeds the tabulated value then the means are significantly different.

10. Now compare your calculated t value with tabulated values for higher levels of significance (e.g. p = 0.01). These levels tell us the probability of our conclusion being correct. For example, if our calculated t value exceeds the tabulated value for p = 0.05, then there is a 95% chance of the means being significantly different (or 99% for p = 0.01, 99.9% for p = 0.001). By convention, we say that a difference between means at the 95% level is "significant", a difference at 99% level is "highly significant" and a difference at 99.9% level is "very highly significant".

What does this mean in "real" terms? Statistical tests allow us to make statements with a degree of precision, but cannot actually prove or disprove anything. A significant result at the 95% probability level tells us that our data are good enough to support a conclusion with 95% confidence (but there is a 1 in 20 chance of being wrong). In biological work we accept this level of significance as being reasonable.

Answer 2

Microsoft Excel. good luck