# Small Random Samples vs one Random Sample (Stratified)

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 -

1. Are the 2 approaches going to give me different distributions (relative to the original distribution)? Would they approximate to the original distribution?
2. 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?
3. 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 -

1. 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.
3. No the way the population of downloads are different (seasonal + random components).
• What are the samples going to be used for? And why do you need samples; why can't you just work with the whole data set? Oct 5, 2015 at 19:24
• Your proposed schemes all ignore "objects". Some questions: 1) What are these? 2 )Will events belonging to the same object be more similar than events belonging to different objects? 3) Can you identify the 1% of high volume objects in advance? Oct 6, 2015 at 0:02
• Another question: is the population of events identical on all days? Oct 6, 2015 at 0:30