in science there are “rules” and “exceptions.” rules provide consensus and patterns, while exceptions provide outliers that challenge (prove) the rules. in hard sciences like physics, a virtual 100% consensus is possible, but in soft sciences like economics, it's never that clean

because all people are cursed with motivated reasoning, if the scientific rule of a subject challenges their beliefs or desires, they actively seek exceptions to reaffirm them. exceptions can sometimes be proven useful, or even correct, but this bias muddies the search for truth

in our recent statements on following consensus and data, not anecdotes and conspiracies, critics added that exceptions do exist. *sometimes* conspiracies or fringe experts are proven true. apologies for lacking more caveats in our message to reestablish trust in expert consensus

unfortunately most data exceptions are promoted with an agenda and propped up by grifters, provocateurs, or useful idiots. skepticism is healthy, but more skeptical weight should be applied to self-proclaimed experts going against expert consensus, than expert consensus itself

to be clear, in some cases it’s vital to evaluate exceptions to the rule, like whistleblowers reporting on unethical practices or marginalized populations within unjust social systems. the credibility or value of an exception to a rule is always dependent on the data subject

credibility shouldn't be fetishized in every industry or area. that said, when it comes to our scientific institutions, we need to reestablish the general population's trust in expert consensus and data, especially during this time of crisis and uncertainty

there will sometimes be experts who go against consensus and end up proven right. there will sometimes be anecdotes that challenge data

but sometimes people take these one-off cases and abandon all trust in our institutions, which is why education on how data works is important

exceptions rarely break rules and rules always have exceptions. in a post-truth era, understanding the scientific method and how our biases play into our data consumption is vital to curb the spread of incomplete or misleading information that the masses then believe as "facts"

genuinely understanding how people think opens the door for change, not lecturing strangers online. data is useless unless it can be shared in the same reality. some will never listen and it's not anyone's job to change others, but facts matter and relationships go a long way

most data around this crisis is incomplete, constantly evolving, and being politically warped, making it near impossible to interpret without context from relevant experts, especially since fear is so prevalent

just try to think critically, be understanding, and do what you can

note: all companies have a bottom line, so anything we publish is a form of propaganda to encourage positive association and memory with our brand, despite whatever our intentions. remember to consume advertising and PR with skepticism, even if the message is "helpful"