We took a gallery walk and learned some startling things:
The more literate a person is, the longer he or she will live. The more literate a woman is (in certain countries) the more at risk she is for poverty. The less wealthy a nation is, the more crimes committed as a result of drug trade.
But we also discounted a lot of these correlations. Instead, we reasoned that the data sets were not necessarily proving cause and effect and that our interpretations needed to be examined. For example, it would be like saying: "It rained on Tuesday and a train crashed." The assumption would then be that any time it rains on a Tuesday a train will crash. Or, one could study how trains crash when it rains on Tuesday, but this does not take into consideration any other factors related to why the train crashed in the first place, and it does not examine what month of the year it is or what region of the world.
The point of collecting data and telling a story is that data can be misconstrued or misrepresented, and as mathematicians, scientists, or storytellers, we have to be very careful about what it is we are suggesting.
We have to be certain that we are not "bending" the truth because we can (clever of us). Another fantastic teachable moment--one that draws on critical thinking and analysis and asks students to explain the data (and the inherent flaws).
1 comment:
Your post is very true and something that needs to be considered anytime we hear statistics or data. It is very easy to fall into the trap of thinking that correlation equals causation. Just because two things are correlated (the train crashes and rain on Tuesday) does not mean that one causes the other to happen. This would definitely be a fun lesson for students to learn while manipulating the data.
Post a Comment