Difference between revisions of "Holcombe:Statistics"
Line 5:  Line 5:  
[http://psychology.uwo.ca/JodyCulham/Courses/ErrorBars_Lecture.ppt Jody Culham error bars lecture]  [http://psychology.uwo.ca/JodyCulham/Courses/ErrorBars_Lecture.ppt Jody Culham error bars lecture]  
+  "Rule of thumb for 95% CIs:  
+  If the overlap is about half of one onesided error bar, the difference is significant at ~ p < .05  
+  If the error bars just abut, the difference is significant at ~ p< .01  
+  works if n >= 10 and error bars don’t differ by more than a factor of 2  
+  "  
"If events are dependent (whether causal or not), the aggregate is not going to be Gaussian. " why?  "If events are dependent (whether causal or not), the aggregate is not going to be Gaussian. " why? 
Revision as of 12:35, 23 March 2010
Members• Alex Holcombe

Projects• Testing Booth Calendar 

Technical• Skills Checklist 
Other• Plots,Graphs

The picturing of data allows us to be sensitive not only to the multiple hypotheses that we hold, but to the many more we have not yet thought of, regard as unlikely, or think impossible  Tukey, 1974
The great fun of information visualization is that it gives you answers to questions you didn’t know you had  Ben Shneiderman
Jody Culham error bars lecture "Rule of thumb for 95% CIs: If the overlap is about half of one onesided error bar, the difference is significant at ~ p < .05 If the error bars just abut, the difference is significant at ~ p< .01 works if n >= 10 and error bars don’t differ by more than a factor of 2 "
"If events are dependent (whether causal or not), the aggregate is not going to be Gaussian. " why?
the sum of two independent random variables is distributed according to the convolution of their individual distributions
Fitting curves to data
 R is often used in the lab
 Python alone and with SciPy can be used easily, example here
 MATLAB is sometimes used
 Using MacCurveFit for OS9; rarely used
Bootstrapping
Howell's pages