http://www.nature.com/collections/qghhqm
Since September 2013 Nature Methods has been publishing a monthly column on statistics aimed at providing reseachers in biology with a basic introduction to core statistical concepts and methods, including experimental design. Although targeted at biologists, the articles are useful guides for researchers in other disciplines as well. A continuously updated list of these articles is provided below.
Importance of being uncertain - How samples are used to estimate population statistics and what this means in terms of uncertainty.
Error Bars - The use of error bars to represent uncertainty and advice on how to interpret them.
Significance, P values and t-tests - Introduction to the concept of statistical significance and the one-sample t-test.
Power and sample size - Using statistical power to optimize study design and sample numbers.
Visualizing samples with box plots - Introduction to box plots and their use to illustrate the spread and differences of samples. See also: Kick the bar chart habit and BoxPlotR: a web tool for generation of box plots
Comparing samples—part I - How to use the two-sample t-test to compare either uncorrelated or correlated samples.
Comparing samples—part II - Adjustment and reinterpretation of P values when large numbers of tests are performed.
Nonparametric tests - Use of nonparametric tests to robustly compare skewed or ranked data.
Designing comparative experiments - The first of a series of columns that tackle experimental design shows how a paired design achieves sensitivity and specificity requirements despite biological and technical variability.
Analysis of variance and blocking - Introduction to ANOVA and the importance of blocking in good experimental design to mitigate experimental error and the impact of factors not under study.
Replication - Technical replication reveals technical variation while biological replication is required for biological inference.
Nested designs - Use the relative noise contribution of each layer in nested experimental designs to optimally allocate experimental resources using ANOVA.
Two-factor designs - It is common in biological systems for multiple experimental factors to produce interacting effects on a system. A study design that allows these interactions can increase sensitivity.
Sources of variation - To generalize experimental conclusions to a population, it is critical to sample its variation while using experimental control, randomization, blocking and replication to collect replicable and meaningful results.
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