Abstract

The proper understanding and use of statistical tools are essential to the scientific enterprise. This is true both at the level of designing one's own experiments as well as for critically evaluating studies carried out by others. Unfortunately, many researchers who are otherwise rigorous and thoughtful in their scientific approach lack sufficient knowledge of this field. This methods chapter is written with such individuals in mind. Although the majority of examples are drawn from the field of Caenorhabditis elegans biology, the concepts and practical applications are also relevant to those who work in the disciplines of molecular genetics and cell and developmental biology. Our intent has been to limit theoretical considerations to a necessary minimum and to use common examples as illustrations for statistical analysis. Our chapter includes a description of basic terms and central concepts and also contains in-depth discussions on the analysis of means, proportions, ratios, probabilities, and correlations. We also address issues related to sample size, normality, outliers, and non-parametric approaches.

Highlights

  • At the first group meeting that I attended as a new worm postdoc (1997, D.S.F.), I heard the following opinion expressed by a senior scientist in the field: “If I need to rely on statistics to prove my point, I'm not doing the right experiment.”

  • It can be posited that analysis of variance32 (ANOVA) is more conservative than uncorrected multiple t-tests and less conservative than Bonferroni methods

  • A question that may arise when comparing more than two binomial proportions is whether or not multiple comparisons should be factored into the statistical analysis

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Summary

Introduction

For many experiments conducted in our field, mean values are not the end goal. We may want to calculate CIs for our sample percentages or may use a formal statistical test to determine if there is likely to be a real difference between the frequencies observed for two or more samples. Our analyses may be best served by determining ratios or fold changes, which may require specific statistical tools. It is often useful, when carrying out genetic experiments, to be able to calculate the probabilities of various outcomes. This section will cover major points that are relevant to our field when dealing with these common situations

Quantifying variation in population or sample data
Confidence intervals
What is the best way to report variation in data?
The coefficient of variation
P-values
One- versus two-sample tests
One versus two tails
Equal or non-equal variances
Is there a minimum acceptable sample size?
Paired versus unpaired tests
The critical value approach
Safety through repetition
The family-wise error rate
Bonferroni-type corrections
False discovery rates
Analysis of variance
Summary of multiple comparisons methods
When are multiple comparison adjustments not required?
A philosophical argument for making no adjustments for multiple comparisons
Calculating simple probabilities
Calculating more-complex probabilities
The Poisson distribution
Binomial proportions
Calculating confidence intervals for binomial proportions
Tests for differences between two binomial proportions
4.10. Tests for differences between more than one binomial proportion
4.11. Probability calculations for binomial proportions
4.13. Tests for differences between multinomial proportions
Comparing relative versus incremental differences
Ratio of means versus mean of ratios
Log scales
Correlation and modeling
Modeling and regression
When is a sample size too small?
Statistical power
Dealing with outliers
Nonparametric tests
A brief word about survival
Fear not the bootstrap
17. Abstract
10. Appendix B
11. Appendix C
Findings
12. Appendix D

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