Suppose one of your friends is ill. The symptoms suggest it to be disease A, a common disease with an 80% probability of occurrence. If it isn't disease A, it can be either disease B or C. There is no way to test for disease A. However, a test can confirm whether the patient is suffering from disease B or C. Each disease has its own treatment, which is ineffective against the others. If you were asked by the doctor, would you test for B and C? A. Yes B. NoIn all probability, your answer is a firm 'yes'. After all, you would want to be sure of the disease before getting your friend treated. In fact, a survey shows that most people agree with you. Jonathan Baron, a professor of Psychology at the University of Pennsylvania, gave the same choices to medical practitioners and found that a majority preferred to perform the test. But, unfortunately, the majority can be wrong as well, just as in this case.
Let us go back to the question to see where your reasoning went wrong. We already know that in 8 cases out of 10 (80%), chances are it is disease A. Also, a positive or negative result will not lower the chances of it being disease A. So, the probability of a patient suffering from disease A is much higher than it being B or C. Also, regardless of what the results are, the patient has to be treated for disease A, making the test superfluous.
| When making a choice, we assume all information is useful and so aren't able to differentiate between 'signals' and 'noise'. |
The subjects in Baron's experiment opted to go for the test without considering how the additional information (the test results) would help them in their choice of the course of treatment. Baron blames this error of judgment on 'information bias'-a tendency to seek additional information without paying attention to the utility of the facts being collected. In other words, people tend to gather information without considering the possible relevance of the data, which often leads to distorted evaluations.
This fallacy is common in the investment industry as well, where everyone seems to be obsessed with statistics and numbers. In their paper 'Harmful Effects of Seemingly Helpful Information on Forecast of Stock Earnings', psychologists Fred D. Davis, Gerald L. Lohse and Jeffrey E. Kottemann asked professional financial analysts to forecast fourth-quarter earnings in 45 cases, while in reality there were only 15 company reports presented in three different formats. Also, each respondent was also asked to give a confidence rating to their predictions. Unfortunately, none of the participants realised that they saw the same reports thrice. In addition, with the increase in size of both significant and non-significant information, there was a rise in number of forecast errors as well. Confidence ratings also increased considerably.
According to Baron, information bias generally occurs when we are confused about our objectives. When making a choice, say while buying stocks, we tend to assume that all information is useful-company reports, expert opinions, news reports, advice from family members and even watercooler discussions with colleagues- without being able to differentiate between 'signals' and 'noise'.
Now, you might say, in a chaotic market, with information pouring in from all directions all the time, how do you differentiate between 'signals' and 'noise'? Decision-making experts have a rather straightforward solution. Make a check-list and keep it simple. They believe you can be a far better decisionmaker if you analyse the five things you really need to know about an investment, rather than trying to analyse everything concerned with it.
