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Small data is sometimes preferable to big data. A high quality small sample can produce superior inferences to a low quality large sample. Data has acquisition, computation and privacy costs which require costs to be balanced against bene- fits. Statistical inference works well on small data but not so well on large data. Sometimes aggregation into small datasets is better than large individual-level data. Small data is a better starting point for teaching of Statistics.
Data is not an end in itself but a means to an end. The end is increased understanding, better calibrated prediction etc. More is not always better if this comes with increased costs. Data is sometimes viewed as something fixed that we have to deal with. It might be better to view it as a resource. We do not aim to use as many resources as possible. We try to use as few resources as possible to obtain the information we need. We have seen the benefit of big data but we are now also realizing the extent of the associated damage. The modern environmental movement started in reaction to the excesses of resource extraction. It advocates an approach that minimizes the use of resources and reduces the negative externalities. We believe the same approach should be taken with data: Small is beautiful.