Typically, this section might address areas relating to (in no particular order):
statistics: split testing, correlation, metric selection, P values, qualitative vs quantitative...
data collection: server side tagging, privacy, user consent, ITP, cross device, marketing attribution, voice of customer, question framing,...
the tech stack: BigQuery, SQL, Python, Looker, GDS, GA4, [Google] Tag Manager,...
There is something else that underpins all of the above, human decision-making bias.
As analysts, data scientists, managers, directors etc, we all exhibit cognitive bias in our decision-making. It's inescapable, and it is the single most likely attribute that can lead us to make poor decisions.
Confirmation bias is common in much of what we do as stakeholders.
Anchoring bias and egocentric bias prevent us from taking on board new evidence and adjusting our position in order to make better decisions. They are present in all hierarchical organisations.
These biases are often interlinked. Being aware of them can help us mitigate their impact and lead to better decision making based on currently available evidence.