Paired T-Interval
When comparing two sets of data, you subtract one from the other to find the difference for every group in the set. These differences are the means of paired data. This is called the Paired T-Test. After finding the mean differences, we want to know how close to the true mean we are for the matched pairs. A Paired T-Interval constructs a confidence interval to estimate the mean difference between matched pairs of data. This confidence level tells us how far away we are from the true mean. Before we can create a confidence interval, we need to check our conditions.
Conditions
Paired Data Condition - the data must be paired.
Independence Assumption - if the data are paired, the groups are not independent. Rather, the differences must be independent of each other.
Randomization Condition - the groups must be random.
Nearly Normal Condition - assume the population of differences follows a Normal Model. We can check this condition with a histogram.
Confidence Equations
If all of our conditions are met, we can calculate a confidence interval:
dbar + t*n-1 X SE(dbar) where dbar is the mean difference,
t* is the critical value,
n-1 finds the degree of freedom, and
SE is the standard error
The standard error of the mean difference is: SE(dbar) = sd /sqrt(n) where sd is the standard deviation of the differences, and
n is the sample size
Steps for Finding the Confidence Interval
In order to conduct the test, we need to follow the appropriate steps to find a solution and come to a conclusion.
Comments (0)
You don't have permission to comment on this page.