Hello,

I have the ticket count for the last 4 months for my IT department.

16391 15525 17516 16966

This is discrete data as this is count of incidents. However, this data does not follow poission distribution and this data is observed to be normal.

I am trying to demonstrate that the decreasing the average monthly ticket count for my IT team to 12000 would be a significant achievement and 12,000 can be the target for my GB project.

So, I am trying to compare the mean of my sample to this constant 12000.

I understand that the 1 sample T test is used for continous data and used to compare sample mean. However, since this data is Normal, am I okay to use 1 sample T test. Would this yield the desired results?

Dear Shiva Kumar,

Since this is discrete data, we usually work with proportions. Is it possible to determine the maximum number of possible ticket counts (trials) and then work with ratios rather than the count of the tickets?

When you are working with proportions (1-Prop test), you will find that you will get similar answers if you make a normal approximation when the number of trials is very large.

SJ.

Hi Suresh Sir,

No, we are unable to determine the maximum possible tickt count. Hence I could not relate the 1P test to this purpose of determining Mean monthly ticket count.

What error would I commit in this specific scenario by using 1 sample T test for this data?

Also, I think we are ok to work with 4 samples as per the power and sample size formula for 1sample T test, would you comment?

 

Regards,

Shiva Kumar

Dear Shiva Kumar,

The count data would probably be a Poisson distribution. It may be better if you take a square root transformation in order to make this data normal. Also, since the numbers are fairly high, it may be okay to assume that the data is continuous rather than discrete.

The sample size of 4 seems too small. What parameters for difference to detect, power, standard deviation did you use to estimate it? With just 4 data points, it would be hard to detect deviations from normality using the normality test - most likely you will accept the null hypothesis indicating that the data is normal. You would be making more type II (beta) error with fewer samples. You could work with daily or weekly data rather than monthly data to increase your number of data points.

Best Regards,

SJ