Backdrop - I will be doing stratified random sampling from a data that is about 100 million events. Distribution of this original data is extreme long tail (1% of objects contribute to 95% of events). I have 2 scenarios for sampling -
Scenario 1 - Doing a stratified random sample of size 100 each day for 30 days. Giving me 30 stratified samples. I combine to get 1 stratified sample of size 3000.
Scenario 2 - At the end of each month, I draw a single 3000 sized stratified sample.
Based on this I have 2 questions -
- Are the 2 approaches going to give me different distributions (relative to the original distribution)? Would they approximate to the original distribution?
- He I have arbitrarily taken 3000 sample size, What should the size of the sample be to represent the original population with say 90% confidence. Is there some equation I can use to calculate this?
- In approach-1; obviously after n days (1≤n≤30) as the cumulative data samples increases the confidence of representing the original 100 million population increases. Is there some way I can know - that after 5 days cumulative sample this data is say 20-25% confident that it represents population. This I want to calculate each day.
All things being same, I would prefer scenario-1 as it increases convenience for me.
Update: to the asked questions, providing more context -
- Each of these samples need to be labelled, hence can't work with entire 100 million events. Also there is a limitation of being able to label on X items per month. But I want to know the confidence of that X.
- These events are "downloads" & each object is "file" being downloaded. Am unsure about download patterns about each file. i.e. don't know if downloads of a file is more similar than downloads belonging to different files. How does this effect sampling? Whatever be the pattern (or bias) in the data I want to accurately capture it in my sample with some x% confidence.
- No the way the population of downloads are different (seasonal + random components).