Specific information can lead to incorrect conclusions if we ignore base rates.
When we encounter specific information, we tend to overlook the general information that is necessary for us to interpret the specific information correctly.
Assume you see someone who is introverted and you're asked if they are a librarian or a salesperson. Since librarians are generally more shy than salespeople, most people would say that, based on the information of introversion, the person is more likely to be a librarian.
However, considering that there are 20 times more salespeople than librarians (base rate), what impact should this information have?
It states that for the specific individual to have a higher chance of being a librarian, introversion needs to be at least 20 times more common among librarians compared to salespeople.
Put into numbers, it becomes clearer: Imagine there are 1,000,000 salespeople and 50,000 librarians. We expect librarians, on average, to be more introverted than salespeople. Let's assume that 50% of librarians are introverted, while only 5% of salespeople are introverted. This means there are 25,000 introverted librarians and 50,000 introverted salespeople.
Now, once more, how probable is it that the quiet person you notice is a librarian? Only half as probable as it being a salesperson (25,000 / 50,000).
Once we consider the overall number of introvert librarians and introvert salespeople, we need to view our specific observations of introversion in a different way.
The same idea applies when we compare hospitalization statistics (specific information) to the general population (base rate). This leads us to different conclusions about health risks.
Assuming you notice that 50% of patients in hospitals have been vaccinated (specific information), it's difficult to evaluate the information accurately without knowing the percentage of vaccinated individuals in the overall population. If 80% are vaccinated and 20% are not vaccinated (base rate), this means that not being vaccinated increases the likelihood of hospitalization by 4 times — even though you may observe a similar number of hospitalizations among vaccinated individuals.
The Base Rate Fallacy is important in policy making, law, finance, entrepreneurship, and any other decision making based on probability or frequency.
Overestimating specific information can lead to incorrect conclusions.
Similarly, understanding base rates and their impact on interpreting observations can help protect against misleading news and media stories.