I want to get back to our discussion of Mathematics and Statistics: 
	
	We've begun talking about mathematics (or, more precisely,
	statistics). We've discussed surveys, and we talked in particular about bias.
	
	I want to add a few things into the mix today. 
	
	
	-  Surveys:
		
		 
		-  Bias: two kinds that we need to avoid:
			
			-  sampling bias
			
-  confirmation bias
			
 
-  We generally survey to capture the essense of some
			quantity: we collect data to create a "proxy" or
			"surrogate", something measurable (but which isn't
			exactly what we want). 
		
 
-  Variables
		
		 
		-  Categorical (e.g. male, female)
		
-  Ordinal (e.g. Likert scales -- from 1 to 5)
		
-  Continuous (e.g. income)
		
 
-  Metrics
		
		 
		-  A metric is a measure of something (maybe
			it's better to think of it as the "measuring stick"). 
		
-  The metric is usually a proxy for something else:
			e.g. schools try to reach certain scores on metrics, to
			show that they're doing a good job.
		
-  Changing units in which data are expressed shouldn't
			change metrics. 
		
 
-  Measures of central tendency: to illustrate, let's collect heights
		of you all. 
		
		 
		-  arithmetic mean (which we will just call the mean)
		
-  median
		
 
-  Variation: things vary from the center. 
		
		 
		-  deviation from the mean/median (sign generally matters!)
			
			 
			-  below average
			
-  above average
			
 
		Do you want to be "below average"? No -- not generally. That's
		an example of a bias introduced through our use of language,
		and a preference for a certain directionality in our use of averages.
		 
		Do you want to be below average on "poverty"?
		 
		 
-  "standard deviation"
		
-  variance
		
-  outlier: something which fails to fit a pattern; 
		
 
-  Normalization:
		
		 
		-  We can combine both central tendency and
			deviation/variation to normalize a variable.
		
-  This allows us to "weight things equally". 
		
 
-  Graphical representation
		
		 
		-  graphs and maps give us a better feeling sometimes for how
		things relate than mere numbers. We'll be playing with a
		computer tool for representing census data next time (I hope -- I'm
		trying to arrange for the computer lab for next time). 
		
-  Graphs
		can be deceiving. We need to be cautious with 
		them. They can also be very powerful.
		
-  In any event, graphical representation will be a very
		important complement to our work with numbers from the census
		bureau.