In the past, mining for gold consisted of choosing a site and then sifting through endless amounts of dirt. Sometimes the miner only found a few valuable nuggets, sometimes he hit upon an entire vein, but most of the time, he found nothing at all and decided to either move on to another promising spot or give up mining altogether and stop wasting his time. Today, with scientific methods and specialised tools, mineral mining is much more accurate and productive. Mining for data and learning from it has evolved in much the same way. Older methods executed by statisticians took a long time to yield constructive information. Now, current software and techniques help make data mining a lucrative and more accessible process in the information age that we live in. The field of data mining, like statistics, concerns itself with `learning from data' or `turning data into information'. So we may want to ask ourselves whether data mining for gold is `statistical déjà vu'.