Faslifiability describes the capacity of a statement to be proven false. For example, the presence of a black swan falsifies the statement "All swans are white." Karl Popper suggested that a focus on falsifiability may enable us to more quickly move from observation to scientific law. That is, it may be impossible to verify that all swans are white; however, finding a swan that is not white quickly invalidates the statement.
Rather than trying to prove that our ideas are correct, we should seek disconfirming evidence.
Sam Ladner suggests we should be skeptical of data that is designed and used without falsifiability in mind. She notes that we collect plenty of data "exhaust" today through internet-of-things devices and app analytics. But, collecting this information alone does not produce knowledge. That requires analysis and interpretation.
- Base strategy on leading, not lagging, indicators
- Four Theories of Truth - Different ways to determine whether a claim is true or not
- Deductive reasoning tests a theory against data - A mode of reasoning that begins with a falsifiable hypothesis and seeks date to validate or invalidate it
"But I often tell people to tamp down their excitement about data exhaust because none of these data are actually designed for falsifiability in mind—it’s simply the detritus of our digital lives. Just because we have more data doesn’t mean we are doing better research."