http://blogs.nature.com/methagora/2013/08/giving_statistics_the_attention_it_deserves.html
http://www.nature.com/collections/qghhqm/pointsofsignificance
Nature网站上面向生物学家的统计教学文章汇总（Statistics for biologists）：
http://www.nature.com/collections/qghhqm/
转自上面第一个网页：
Importance of being uncertain – September 2013
How samples are used to estimate population statistics and what this means in terms of uncertainty.
Error Bars – October 2013
The use of error bars to represent uncertainty and advice on how to interpret them.
Significance, P values and ttests – November 2013
Introduction to the concept of statistical significance and the onesample ttest.
Power and sample size – December 2013
Using statistical power to optimize study design and sample numbers.
Visualizing samples with box plots – February 2014
Introduction to box plots and their use to illustrate the spread and differences of samples.
Comparing samples—part I – March 2014
How to use the twosample ttest to compare either uncorrelated or correlated samples.
Comparing samples—part II – April 2014
Adjustment and reinterpretation of P values when large numbers of tests are performed.
Nonparametric tests – May 2014
Use of nonparametric tests to robustly compare skewed or ranked data.
Designing comparative experiments – June 2014
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 – July 2014
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 – September 2014
Technical replication reveals technical variation while biological replication is required for biological inference.
Nested designs – October 2014
Use the relative noise contribution of each layer in nested experimental designs to optimally allocate experimental resources using ANOVA.
Twofactor designs – December 2014
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 – January 2015
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.
Split plot design – March 2015
When some experimental factors are harder to vary than others, a split plot design can be efficient for exploring the main (average) effects and interactions of the factors.
Bayes’ theorem – April 2015
Use Bayes’ theorem to combine prior knowledge with observations of a system and make predictions about it.
Bayesian statistics – May 2015
Unlike classical frequentist statistics, Bayesian statistics allows direct inference of the probability that a model is correct and it provides the ability to update this probability as new data is collected.
Sampling distributions and the bootstrap – June 2015
Use the bootstrap method to simulate new samples and assess the precision and bias of sample estimates.
Bayesian networks – September 2015
Model interactions between causes and effects in large networks of causal influences using Bayesian networks, which combine network analysis with Bayesian statistics.
Association, correlation and causation – October 2015
Pairwise dependencies can be characterized using correlation but be aware that correlation only implies association, not causation. Conversely, causation implies association, not correlation.
Simple linear regression – November 2015
Linear regression is a flexible way to predict the values of one variable using the values of the other to find a ‘best line’ through the data points.

