- Search your feelings
- Excited, for or against, can be a sign of bad data
- Ponder your personal experience
- “Naïve reality” is when we think our perceptions are more accurate than they really are (most often because the news weights our perceptions to the novel)
- We often substitute a hard question for an easier one, without knowing it
- (Instead of “how many pregnant teenagers are there?” we ask “have I recently experienced a news story about pregnant teenagers”)
- Be careful of how metrics are being leveraged
- Avoid premature enumeration
- Definitions can skew enumerations
- Start by understanding what was meant
- Once you learn something, it is difficult to remember that everyone else is not familiar
- Inference is an easy way
- Step back and enjoy the view
- Carrying around reference numbers can help context
- Good news tends to unfold slowly while bad news happens radiply
- Reporting cadence can have a powerful effect on context
- Get the backstory
- Survivorship bias pervades… No one is interested is the expected
- This is especially true with replication studies
- Be wary of hypothesis made after the study is started
- Ask who is missing
- Females are almost always missing from studies
- Almost every audience (especially “found” data) has response bias
- “n=all” usually really means “n=all the people who do or have a thing“
- Demand transparency when the computer says “No”
- Look for algorithm’s explanatory power or else they may be associating “winter trends” with flu cases
- Algorithms are built by human and so can easily have bias built-in (hiring algorithms trained to hire men)
- Transparency is what killed alchemy and brought science
- We should question which algorithms we can trust
- Don’t take statistical bedrock for granted
- Basic understandings are important for building senses of the world
- Remember that misinformation can be beautiful, too
- Graphics lend authenticity, the more beautiful the more believable
- Florence Nightingale was a statistician
- Keep an open mind
- We naturally fill in data blanks
- “Things are going badly, so do something different”
- Making public commitments makes it harder to take back
- When information changes, change your conclusions
- Be curious
- Confirmation bias is real and powerful
- Scientific curiosity is key (not scientific literacy)
- Asking people to rate their knowledge, then explain how something works, can reduce stubbornness
- “Please explain that” can do a lot to soften extreme stances
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