Holcombe:Statistics: Difference between revisions

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[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 one-sided 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

Recent members

Alex Holcombe
• Ryo Nakayama



Technical

Skills Checklist
Python Programming
Psychopy/VisionEgg Installation Notes
R analysis,plot,stats
Statistics
Buttonbox
Buttonbox with photocell
Programming Cheat Sheets


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 one-sided 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

Circular Statistics