Spurious Correlation or Spurious Correlation is the concept when strong association exist between unrelated variables. This makes a lot of sense to develop the caution that not all research findings can actually be theoretical viable so authors need a strong theoretical logic and practicality to define the association. Spurious regression has been very well document by Tyler Vigen (copy paste the link https://tylervigen.com/spurious-correlations if the link does not seems to work) and there are very interesting updates. We recommend their portal be visited on regular basis for evidence of spurious correlation on interesting cases.
Few examples of Spurious Correlation from Vigel’s portal are presented in the following:
Margarine consumption linked to divorce is the first interesting example of spurious correlation between two very unrelated but shocking alarming indicators from real life. It is also what BBC has mention in their report mentioning some other examples from Tyler Vigel portal. Nicolas Cage is one top Hollywoord star. He has been mentioned on Tyler Viger series on spurious correlation with an example mentioning Nic’s movies appearances and people drowning in ponds in US States. Similarly, we find the spurious correlation showing the association between consumption of cheese and people died through folding in their bed sheets.
Similarly, we Economists needs very strong care to define relationship between economic indicators. Theoretically and technically very unrelated time series might appear to be very strongly correlated to each other but that might be of the no use case. Hence, defining an econometric models merely from available data does not meaning anything of significance but one should all the three components of research significance profoundly available for witness. These three indicators should include:
- Whether the relationship has any significance for solving a real world problem.
- The relationship found should be able to predict too much of the theory.
- The data should be very strongly related in process of generation. If the data generating processes are not correlated to each, the chances of spurious correlation or spurious regression again increases.
Now enjoy the few cases of Spurious correlation found by Vigel here: https://tylervigen.com/spurious-correlations