Explaining XKCD and PhD Comics for Data Evaluation

* Explaining the joke!

Do you know why these are funny?

At the end of the class you should be able to explain the basis for the jokes!

Phdcomics/xkcd:

* The difference (scientist) https://m.xkcd.com/242/

* Convincing (label axes) https://m.xkcd.com/833/

* The actual method http://www.phdcomics.com/comics/archive.php?comicid=761

* Correlation https://m.xkcd.com/552/

* Cancer causes cellphones https://m.xkcd.com/925/

* Blind Trials https://m.xkcd.com/1462/

* Experimentation Lesbian Control Group B https://m.xkcd.com/507/

* Fasters Growing https://m.xkcd.com/1102/

* Decline Graphing Coincidence https://m.xkcd.com/523/

* Linear Regression https://m.xkcd.com/1725/

* Null Hypothesis https://m.xkcd.com/892/

* p-Values https://m.xkcd.com/1478/

* Significance jelly beans https://m.xkcd.com/882/

* Statistically significant boyfriend https://m.xkcd.com/539/

* ANOVA Analysis of Value http://www.phdcomics.com/comics/archive.php?comicid=905

* Frequentist vs. Bayesian https://m.xkcd.com/1132/

* Science Valentine https://m.xkcd.com/701/

* What have you done / used in terms of statistics for research? What for exactly?

* Statistical/quantitative evaluation terms:

* Measurements, variables, random samples and errors

* Independent observations

* Representative observations

* Distributions of observation (normal distribution)

* Average and mean

* Variance and standard deviation

* Standard error of the mean

* Effect size

* Degrees of freedom

* Confidence interval

* Covariance (relative independence of measurement error for derived quantities)

* hypothesis testing

* null hypothesis

* t-test

* ANOVA, ANCOVA

* Regression analysis, model calibration, data fitting, curve fitting

* Priors, prior probability, Bayesian estimates

* Transparent statistics in HCI