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, and meta-statistics. It explains how to compare tree heights, perform ANOVA, and interpret F-distribution. Worked examples and assumptions of ANOVA are discussed. The lecture also delves into post-hoc tests like the Scheffé test and repeated measures ANOVA. A hypothetical scenario involving superpowers is used to illustrate statistical concepts. The importance of accurately reporting statistical values and possible solutions to reduce reporting errors are highlighted.

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

Related concepts (32)

Repeated measures design

Repeated measures design is a research design that involves multiple measures of the same variable taken on the same or matched subjects either under different conditions or over two or more time periods. For instance, repeated measurements are collected in a longitudinal study in which change over time is assessed. Crossover study A popular repeated-measures design is the crossover study. A crossover study is a longitudinal study in which subjects receive a sequence of different treatments (or exposures).

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".

Probability theory

Probability theory or probability calculus is the branch of mathematics concerned with probability. Although there are several different probability interpretations, probability theory treats the concept in a rigorous mathematical manner by expressing it through a set of axioms. Typically these axioms formalise probability in terms of a probability space, which assigns a measure taking values between 0 and 1, termed the probability measure, to a set of outcomes called the sample space.

Probability

Probability is the branch of mathematics concerning numerical descriptions of how likely an event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and 1, where, roughly speaking, 0 indicates impossibility of the event and 1 indicates certainty. The higher the probability of an event, the more likely it is that the event will occur. A simple example is the tossing of a fair (unbiased) coin.

Post hoc analysis

In a scientific study, post hoc analysis (from Latin post hoc, "after this") consists of statistical analyses that were specified after the data were seen. They are usually used to uncover specific differences between three or more group means when an analysis of variance (ANOVA) test is significant. This typically creates a multiple testing problem because each potential analysis is effectively a statistical test. Multiple testing procedures are sometimes used to compensate, but that is often difficult or impossible to do precisely.

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Repeated measures design