Lecture

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 topics such as conditional probability, statistics via signal detection theory, Framingham studies on education level and blood pressure, effect size in statistics, t-test, undersampling, sampling error, sampling distributions, and implications of sample size and effect size 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)

Sampling (statistics)

In statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample (termed sample for short) of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt to collect samples that are representative of the population. Sampling has lower costs and faster data collection compared to recording data from the entire population, and thus, it can provide insights in cases where it is infeasible to measure an entire population.

Stratified sampling

In statistics, stratified sampling is a method of sampling from a population which can be partitioned into subpopulations. In statistical surveys, when subpopulations within an overall population vary, it could be advantageous to sample each subpopulation (stratum) independently. Stratification is the process of dividing members of the population into homogeneous subgroups before sampling. The strata should define a partition of the population.

Statistical significance

In statistical hypothesis testing, a result has statistical significance when a result at least as "extreme" would be very infrequent if the null hypothesis were true. More precisely, a study's defined significance level, denoted by , is the probability of the study rejecting the null hypothesis, given that the null hypothesis is true; and the p-value of a result, , is the probability of obtaining a result at least as extreme, given that the null hypothesis is true. The result is statistically significant, by the standards of the study, when .

Sampling error

In statistics, sampling errors are incurred when the statistical characteristics of a population are estimated from a subset, or sample, of that population. It can produced biased results. Since the sample does not include all members of the population, statistics of the sample (often known as estimators), such as means and quartiles, generally differ from the statistics of the entire population (known as parameters). The difference between the sample statistic and population parameter is considered the sampling error.

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 .

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