# ILP Statistical Analysis

## Contents

## Introduction

Quantitative experimental data will generally need to be analysed using some form of statistics. There are a large number of different statistical tests that can be used depending upon your data, how it was collected and the types of comparisons you intend to make.

The information shown below is extracted from the "information for authors" for Nature Cell Biology submissions.

### Nature Cell Biology - Guide to Authors and Referees

### Statistics

The description of all reported data that includes statistical testing must state the name of the statistical test used to generate error bars and P values, the number ( n ) of independent experiments underlying each data point (not replicate measures of one sample), and the actual P value for each test (not merely 'significant' or 'P < .05').

Descriptive statistics should include:

- clearly labeled measure of center (such as the mean or the median)
- clearly labeled measure of variability (such as standard deviation or range)
- Ranges are more appropriate than standard deviations or standard errors for small data sets. Standard error or confidence interval is appropriate to compare data to a control.

- Graphs should include clearly labeled error bars.
- Authors must state whether a number that follows the ± sign is a standard error (s.e.m.) or a standard deviation (s.d.).

Since for complex biological experiments the number of independent repeats of a measurement often has to be limited for practical reasons, statistical measures with a very small n are commonplace.

- Statistical measures applied to too small a sample size are not significant and they can suggest a false level of significance.
- Error bars should not be provided for n < 3. Instead, the actual individual data from each experiment should be plotted
- If n < 5 individual data should be plotted alongside an error bar.
- In cases where n is small, a justification for the use of the statistical test employed has to be provided.

It is admissible to present a single 'typical result' of n experiments.

- If n is not based on independent experiments (that is n merely represents replicates of a measurement), it may still be meaningful to present statistics, but a detailed description of the repeated measurement is required.

A basic description of n, P and the test applied should be provided in the figure legends, and a further discussion of statistical methodology should be provided in the methods section.

Authors must justify the use of a particular test and explain whether their data conform to the assumptions of the tests.

### Common Errors

**Multiple Comparisons**

When making multiple statistical comparisons on a single data set, authors should explain how they adjusted the alpha level to avoid an inflated Type I error rate, or they should select statistical tests appropriate for multiple groups (such as ANOVA rather than a series of t-tests).

**Normal Distribution**

Many statistical tests require that the data be approximately normally distributed; when using these tests, authors should explain how they tested their data for normality. If the data do not meet the assumptions of the test, then a non-parametric alternative should be used instead.

**Small Sample Size**

When the sample size is small (less than about 10), authors should use tests appropriate to small samples or justify their use of large-sample tests.

**Text extract source:** Nature Cell Biology - Guide to Authors and Referees