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WeDriftEternal t1_j9luxw0 wrote

If not more is reasonable too, I kept it easy in the last comment that 20-25% is a reasonable assumption, but considering the error rate and type of survey, our estimates likely are on the lower bound than the middle or upper bound, so "if not more" in my comment indicates potential spot on the curve is on the left, but we don't know where, but the assumption is that we are not at the upper bound, and I provided a range estimate as well acknowledging error and unknown

FYI 16.9% is the 65+ total US population, in this study it was only looking at 18+, so you adjust the population and 65+ is around 20% of US 18+ population (its actually about 21%-22%, depending on what metrics you use, and I don't have the methodology of the survey, so using 20% as an assumption is fine, as it even underestimates, so teh comment about "if not more" is even more valid since i underestimated the "no" group)

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PitbullMandelaEffect t1_j9lvh3a wrote

Just out of curiosity, why are we excluding the 65+ population? Do you not consider them people or something.

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WeDriftEternal t1_j9lwech wrote

65+ are far less likely to engage in digital priacy.

Here's a study example showing only 2% of 65+ are involved in digital piracy. In other words, a significant part of our "no" category is likely coming from this particular group. This also shows piracy leans towards younger audiences, for example in this survey, a 18-29 year old is 10x more likely to engage in piracy than a 65+ gives us a lot of indication that the 65+ audience isn't a major factor and we may be able to get better insight if we exclude them

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PitbullMandelaEffect t1_j9m1r1j wrote

Yes, if you remove the segments of the population least likely to pirate, the data will show pirating is more common. Exactly what “insights” are you attempting to find here? Are you just trying to pump the number up as high as possible?

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WeDriftEternal t1_j9m5zyr wrote

This is how you do analysis. You look at the data and see what additional insights you can draw, you evaluate your original assumption and update it from the new data.

For example, we have what is likely an outlier group here (we actually probably have 2, 1 young 1 old). And you look at what the data looks like without the outlier. Does this provide more insight? Is there something unusual about the group you excluded that makes it necessary or not necessary to include. What do these separated data tell you now? Was this group just padding "no's"? Were they even relevant to ask or should we assume they never were going to be "yes's".

Its one step of many to the next iteration of analysis and research.

Additionally, its pretty common to bucket out 18-64 in a lot of research, especially things like media (media often uses 18-54, 55+, or 65+). Even moreso, we have a pretty good understanding of the 65+ group in media much more than younger groups in how they consume and spend on media (there's lots to say here but its getting deep technical).

In otherwords, there isn't juicing, what you want to do is see what the hell is happening, and adjusting the data to look at it from different angles is one step and seeing where things fit.

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