Availability (bias) is a heuristic; whereby people make judgments about the likelihood of an event based on how easily an example, instance, or case comes to mind. We all experience availability bias in everyday dialogue, but especially in marketing when we hear; "but, you know it's much harder to get data about that”, and this is often true. This emphases that it must be taken into consideration that there is an awareness and acknowledgement that the data being used may not necessarily answer your question.
It depends on how important the question that you're trying to answer is; if it's a high-stakes decision, you should be creative about collecting various sources of data to avoid this trap.
Every decision we make is influenced by our emotions, and when high pressure situations are present, it's very easy for our decision-making judgment to get clouded by these emotions, especially in group environments.
This relatable trap simply emphasises the importance of taking a moment to assess the external view. By allowing some distance from short-term pressures and emotions, we can assess the data without having our judgement being clouded and distracted. These windows create an opportunity for emotional intelligence (EQ) to flourish, where awareness and mindfulness will act as the key deterrent to this trap.
There is room for both emotions and data, particularly in advertising and ideation. However, it's often the case that the former can outweigh the latter, when the pressure is very high, and that's precisely what we need to avoid.
Confirmation bias is the tendency to process information by looking for, or interpreting, information that is consistent with one’s existing beliefs. This biased approach to decision making is largely unintentional and often results in ignoring inconsistent information.
We're all very likely to fall into confirmation bias for the simple reason that it makes us feel intelligent. When we pursue a risky avenue, we seek people who will give us positive feedback and encourage us which is precisely what we want in terms of building our confidence. At the same time this should not be interpreted as objective evidence. And it's not necessarily what we need to make a good decision.
What we actually need is the disconfirming evidence, which is evidence that refutes an opinion or forecast. Interestingly disconfirming evidence is regularly overlooked in data analysis and widely-considered to be the most underused.
Overconfidence is ever pervasive in advertising, and is arguably a requirement in this field to validate pitches or concepts. However, it’s a pitfall in the data science world, even for people whose jobs are to look at data and be objective. While we don't want people who aren't hopeful, as we would never have those breakthrough findings, at the same time, we need to stay objective and understand when something works and when something doesn't. Therefore, we can see precisely why being able to use data in the right way is so critical.
Psychologically, the framing effect is a cognitive bias where people decide on options based on whether the options are presented with positive or negative connotations. When we use analytics, we can clearly define the objectives and the boundaries of those projects from the start. And this is not just important for our own understanding, it's also critical for our peers so everybody can build a common consensus about what is it that we're trying to achieve, how we measure success and what might be some of the limitations of our analysis. When we suffer from a narrow frame effect, big data and advanced analytics can help us widen our frame because we can explore directions or hypothesis that normally we wouldn't be able to simply because we wouldn't have the opportunity to collect this data and infer patterns from them.
The general consensus indicates that the main obstacle for good decision-making is that we're unaware of our own biases. If we think about common behaviour in the workplace, such as seeking positive feedback and recalling this only, we can see how our thinking can be susceptible to these traps. However, as we know, avoiding negative feedback results in missing important insight for continued learning and development, a sentiment that is echoed greatly in data analysis.