Im with a doubt about calculation and interpretation of confidence intervals.
I have a dataset that has some response times measurements from some requests send to some servers. And I want to calculate confidence intervals for this response time variable. This variable is in miliseconds.
I read that we should use a t-table to calculate confidence intervals, but Im using SPSS and I went to the "explore" menu and I selected "Descriptives" with 95% confidence interval.
And I get this results:
Mean: 904,73
95% confidence interval: Lower Bound: 900,48
Upper Bound: 908,98
My first doubt is: Using spss for this calculation we dont need to use the t-table right?
And the main doubt is about the interpretation of this value. This means what? If we measure again the response times of the requests in the same scenario the values will be in that values(900,48 908,98) with 95% confidence? Sorry, maybe this is a basic question but Im not understanding well the meaning of this confidence interval values.
Response time stats:
Standard Deviation: 1602
Variance: 2568399
Kurtosis: 37194
Skewness: 335900
Depending on your sample size, the distribution may be approximately normal which would allow you to use the Standard Normal Probabilities Table (which has critical z numbers).
Interpretation: We are 95% confident that the true mean value of server response time is between 900,48 and 908,98 seconds.