My previous post made the point that it’s not a matter of whether it is good for you to use samples, but how good the sample you are using is. The comments on that post raised two different, and valid, perspectives about sampling. These viewpoints reflected two different use cases for data,
In my previous post, I discussed sampling error (i.e., when a randomly chosen sample doesn’t reflect the underlying population, aka margin of error) and sampling bias (i.e., when the sample isn’t randomly chosen at all), both of which big data advocates often claim can, and should, be overcome by using all the data. In this
In his recent Financial Times article, Tim Harford explained the big data that interests many companies is what we might call found data – the digital exhaust from our web searches, our status updates on social networks, our credit card purchases and our mobile devices pinging the nearest cellular or WiFi network.
