I'm looking at some Facebook advertising data. It has a row for each ad and the upper and lower bounds of the impressions for that ad (i.e. number of times it was displayed). For example:
ad_id impressions_lower impressions_upper 1 0 999 2 1000000 NaN # No upper bound stated 3 2000 2999 4 35000 35999 5 0 999 6 1000 1999 7 0 999 8 0 999 9 0 999
I want to estimate roughly how many times each ad was viewed, as a single number, to give a rough idea of the distribution of impressions and the total number of impressions.
I could take the simple midpoint of the impressions and use that.
But the overall distribution of the data is not normal: there are many more ads with a small number of impressions than with a large number of impressions. I guess it's a Poisson distribution?
So perhaps I should pick, say, 20% of the way along the range; or 30% along; or 40%. I'm not a statistician, but I feel that there must be a way to choose what's a reasonable number.
Of course, I will report the upper and lower error bars too: but it would be very useful to have a reasonable, rule-of-thumb single estimate for number of impressions for each ad.