Lecture

Understanding Statistics & Experimental Design

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In course

BIO-449: Understanding statistics and experimental design

This course is neither an introduction to the mathematics of statistics nor an introduction to a statistics program such as R. The aim of the course is to understand statistics from its experimental design and to avoid common pitfalls of statistical reason ...

Description

This lecture covers basic probability theory, signal detection theory, statistics in a nutshell, multiple testing, ANOVA, experimental design, correlations, PCA, meta-statistics, statistical power analysis, conventions about statistical power, retrospective power analysis, t-tests, central limit theorem, metrics, confidence intervals, non-parametric tests, hypothesis testing procedures, popular non-parametric tests, advantages and disadvantages of non-parametric tests, and good and bad practices in statistical analysis.

Instructor

Michael Herzog

Teaching & PhD PhD Students Sandrine Brikh, Melissa Mohamad Allouche, Ece Yatikçi, Melissa Faggella, Elsa Scialom, Marie Isabelle Holdsworth, Martina Morea, Pierre Merret, Sandali Nisansa Liyanagoonawardena Past EPFL PhD Students Frank Scharnowski, Thomas Otto, Toni Saarela, Elisa Tartaglia, Bilge Sayim, Johannes Rüter, Marco Boi, Kristoffer Aberg, Yvonne Evelina Thunell, Mauro Manassi, Vitaly Chicherov, Lukasz Grzeczkowski, Marc Michael Lauffs, Ophélie Favrod, Janir Nuno Ramos da Cruz, Adrien Doerig, Lei

Official source

Ontological neighbourhood

Statistics

Statistical inference: Statistical hypothesis testing, Mathematical statistics

Related concepts (32)

Statistical hypothesis testing

A statistical hypothesis test is a method of statistical inference used to decide whether the data at hand sufficiently support a particular hypothesis. Hypothesis testing allows us to make probabilistic statements about population parameters. While hypothesis testing was popularized early in the 20th century, early forms were used in the 1700s. The first use is credited to John Arbuthnot (1710), followed by Pierre-Simon Laplace (1770s), in analyzing the human sex ratio at birth; see .

Statistics

Statistics (from German: Statistik, () "description of a state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal".

Power of a test

In statistics, the power of a binary hypothesis test is the probability that the test correctly rejects the null hypothesis () when a specific alternative hypothesis () is true. It is commonly denoted by , and represents the chances of a true positive detection conditional on the actual existence of an effect to detect. Statistical power ranges from 0 to 1, and as the power of a test increases, the probability of making a type II error by wrongly failing to reject the null hypothesis decreases.

Standard error

The standard error (SE) of a statistic (usually an estimate of a parameter) is the standard deviation of its sampling distribution or an estimate of that standard deviation. If the statistic is the sample mean, it is called the standard error of the mean (SEM). The sampling distribution of a mean is generated by repeated sampling from the same population and recording of the sample means obtained. This forms a distribution of different means, and this distribution has its own mean and variance.

Analysis of variance

Analysis of variance (ANOVA) is a collection of statistical models and their associated estimation procedures (such as the "variation" among and between groups) used to analyze the differences among means. ANOVA was developed by the statistician Ronald Fisher. ANOVA is based on the law of total variance, where the observed variance in a particular variable is partitioned into components attributable to different sources of variation.

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