• experimental design
• data exploration
• statistical tests & assumptions
• analysis platforms

source code

• random assignment to treatments
• poorer alternative: haphazard assignment
• stratification
  (i.e., randomize within groups)
• related: experimental blinding

• need to guess effect size and variability
  • biological common sense
  • previous studies
  • ask your supervisor
• OK to simplify design (e.g. ANOVA \(\to\) \(t\)-test)
• methods
  • seat-of-the-pants
  • web calculators
  • in R

apropos("^power")  ## base-R functions
library("sos"); findFn("{power analysis}")

• maximize desired variation
  • e.g. large doses (tradeoff with biological realism)
• minimize undesired variation
  • within-subjects designs
    • e.g. paired, randomized-block, crossover
  • minimize environmental variation
    • tradeoff with generality
    • e.g. environmental chambers;
      clonal or inbred lines
• isolate desired effects: positive/negative controls
  (vehicle-only, cage treatments, etc.)
• control for variation statistically (e.g. ANCOVA)

• check that values are reasonable
• categorical
  • tabulation, cross-tabulation
• univariate
  • mean, standard deviation
  • median, minimum, maximum
• bivariate
  • correlations

• univariate
  • histogram
• grouped
  • box-and-whisker (violin)
• bivariate
  • scatterplots
• multivariate
  • scatterplot matrices

• independence (hard to test!)
• homogeneity of variance (vs. heteroscedasticity)
• linearity
• Normality (least important)
  • outliers; skew; "fat tails" (Student, 1927)
  • distributional assumptions apply to the conditional distribution of the response variable

• hypothesis tests are not generally appropriate:
  they answer the wrong question
• graphical diagnostics
  • residuals plots (linearity, heteroscedasticity)
  • influence plots (outliers)
  • Q-Q plots (Normality)
  • Box-Cox plots (transformations)

• drop outliers (report both analyses)
• transform (e.g. log transform: Box-Cox analysis)
• non-parametric (rank-based) tests
  (e.g. Mann-Whitney-Wilcoxon, Kruskal-Wallis)
• relax assumptions/do fancier stats, e.g.
  • logistic regression (0/1 outcomes)
  • quadratic regression (nonlinearity)

• try to connect scientific & statistical questions
• data type
  • see decision tree or table
  • if your question doesn't fit in this tree,
    think about how much you like statistics …
• nonparametric stats
  • slight loss of power
  • stronger assumptions than you think
  • \(p\)-values only - no effect size

• simple/weak vs. complex/powerful
• GUI vs command-line
• default: use what your lab uses

• ubiquitous
• open alternatives (Open Office)
• data in plain sight
• good enough for simple stuff
• occasional traps (McCullough & Heiser, 2008)
• archive your data as CSV, not XLSX

• SPSS, JMP, SAS, …
• more reliable than Excel
• more powerful than Excel
• point & click (mostly)

• powerful; free & open
• reproducible: script-based
• hardest to learn
• R Commander if you need a GUI
• great for data manipulation, graphics
  (once you learn how)

Using this analogy programs like SPSS are busses, easy to use for the standard things, but very frustrating if you want to do something that is not already preprogrammed.

R is a 4-wheel drive SUV (though environmentally friendly) with a bike on the back, a kayak on top, good walking and running shoes in the passenger seat, and mountain climbing and spelunking gear in the back. R can take you anywhere you want to go if you take time to learn how to use the equipment, but that is going to take longer than learning where the bus stops are in SPSS.

Greg Snow, R-help (May 2006)

x1 = c(1.5,2.5,2.1)
x2 = c(1.1,1.4,1.5)
t.test(x1,x2)

## 
##  Welch Two Sample t-test
## 
## data:  x1 and x2
## t = 2.226, df = 2.6648, p-value = 0.1236
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.3759079  1.7759079
## sample estimates:
## mean of x mean of y 
##  2.033333  1.333333

• Nature web collection, "Statistics for Biologists"
• UCLA statistics consulting (many examples in SAS, SPSS, R …)
• CrossValidated