In itself it is optimistic to say, as I do in my videos, that various domains of statistics, or data science, help in making more rigorous decisions, which lead to increasing turnover and/or decreasing costs. This is because we do not live in an era where we have automated decision makers. We don’t even have this situation in industrial chemical plants, although there we have pumps and valves that “decide” to turn on/off when they receive certain information.
Entrepreneurs and managers, as human beings, have a personality and consequently it is not enough to communicate the results of certain statistical analyzes in order to convince them to make certain decisions, or make them change certain repeated decisions, i.e. a strategy.
Unfortunately I am delving into psychology and will end up in psychometrics because statistics, from being an auxiliary science, inevitably ends up in other areas.
What cognitive limits block entrepreneurs and managers? Why don’t they act based on statistical evidence?
I will deal with a topic that has been known for about ten years now: the contents of the book “Thinking, Fast and Slow”, by a Nobel Prize winner in economics who essentially demolished a strong hypothesis of the old economic models: the theory of the efficient market, and therefore the existence of rational participants. In this case entrepreneurs and managers.
When I read it, in my fourth year of high school, I was shocked by a sentence from the author, who says that knowing the existence of cognitive biases, i.e. prejudices, distortions or cognitive limits, DOES NOT defends against committing them. So useless reading? Largely not. In other words, it seems like knowing about the existence of mosquitoes and being able to do almost nothing to defend yourself. An almost fatalism that smacks of an uncomfortable truth.
Some recurring cognitive limitations of entrepreneurs and managers
confirmation bias
analyzes only aimed at confirming the hypothesis of the entrepreneur or manager. In other words, paying the statistician to not stay out of your comfort zone. A sort of mirror of desires. The statistician is reduced to a mere executor, a technician, canceling them out as a scientist. In fact, some call data scientists “business scientists”. Personally I find it a little cringe.
Or the empirical evidence of certain statistical results is denied.
anchoring bias
the owner sets a turnover target x for the current year. As anchor we have this. The statistician points out that this is unrealistic, for example looking at the average of aggregate sales per month, taking into account uncertainty (variance) or in some cases objective limits such as business and/or market bottlenecks (demand).
Technological anchoring, linked to other biases that follow: companies become fossilized / anchored with Excel despite having few specific reasons to prefer it to Google spreadsheets.
availability bias
of owners and managers rely on intuition because a statistical analysis is less available in terms of accessibility of vocabulary, analysis time, costs linked to the analysis, etc. Why complicate the bread?An old Italian singer would say.
Intuition can also be replaced with familiarity with a certain decision-making process, analysis software, etc.
sunk cost
the owner spends a 5-figure amount for a certain sales strategy in a more competitive market but with potentially greater results. The statistician, or data scientist, says that there are safer markets. The owner, to protect their own integrity, they do not listen to the statistical point of view despite they have no results in that market. It would cost them too much in terms of self-esteem to admit the mistake.
Another technological example: the company does not want to change the management system despite causing technical debt. For example, recovering data from that management system, instead of having a CRM (a kind of database for potential customers, customers and more), costs much more because it requires a person, as data cannot be extracted programmatically and automatically. If you want, you can automate the person’s work, but it still costs more than changing the approach (software).
status quo or inertia
one of the reasons why we still need to talk about digital transformation in SMEs. Fortunately, large consultancy firms do this type of work.
This limit acts as a substrate for the previous cases.
Types of entrepreneurs and managers most prone to bias
And here we get into psychometrics, which I also mentioned in an (Italian) episode of the podcast and in a reel in Italian. If we take the most accepted quantification of personality in the literature, “The 5 personality traits”, also called OCEAN, we cannot easily find what we want for the categories of people mentioned.
However, on a general level:
- high levels of Openness (O, or open-mindedness) lower the risk of confirmation bias (according to the OCEAN theory) but increase overconfidence (Kumar et al. 2021).
- high levels of extraversion (E) increase the risk of availability bias, overconfidence (Ahmad, 2020; Singh et al., 2022)).
- high levels of neuroticism (N) increase the risk of availability bias, anchoring (Singh et al, 2022). For clarity, high N means emotional instability, low N means strong temper.
However, this is a poorly explored area of research, in fact the studies are few and with a small or too sectoral number of participants (e.g. investors).
What can you do?
First of all online you can find tests that give you an idea of your OCEAN profile, though won’t replace the diagnosis of an expert.
Once you become aware of your limits, you can find a useful conscience cricket, as one review says, in STATiCalmo. But let’s get to know each other first in a free call. Then, perhaps, we can proceed with statistical consultancy based on data analysis and more.
If you have a limited liability company (Ltd), the partners can balance your choices, if the statistician informs all members of the results of the analyses.
If you have a public limited company (PLC), presenting the results to the majority shareholders can avoid bad days on the stock market following decisions taken but not shared after the fact.
Do you understand where I’m going with this? As is also seen in democracies, having more participants in decisions helps to balance biases. A bit like when, in statistics, an assembly of statistical models is used in order to improve the prediction of an objective variable (e.g. conversion). However, having more participants creates more decision-making bureaucracy. So we arrive at a dilemma: speed of execution but tyranny or decision-making bureaucracy but pluralism.