Lecture

GLM: Statistical Hypothesis Testing

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In course

NX-421: Neural signals and signal processing

Understanding, processing, and analysis of signals and images obtained from the central and peripheral nervous system ...

Description

This lecture by the instructor covers the efficiency of a design in neural signals and signal processing, focusing on the concept of partial correlation and the application of the General Linear Model (GLM) to voxel timecourses. It delves into statistical hypothesis testing within GLM, explaining the derivation of contrasts and the assessment of evidence for specific hypotheses. The lecture also explores error types in GLM, including false positives and false negatives, and discusses the importance of thresholding and multiple comparisons in statistical analysis.

Instructors (2)

Dimitri Nestor Alice Van De Ville

Expertise Brain and spinal cord imaging, signal and image processing, network science, dynamic systems Teaching & PhD PhD Students Alexandre Cionca, Chun Hei Michael Chan, Flavia Petruso, Bassam El Rawas, Nikolaos Molochidis, Christophe Pascal Jérémie Chênes, Fabrice Jean Guibert, Alessio Siviglia, Ekansh Sareen, Beatrice Lugli Past EPFL PhD Students Djano Kandaswamy, Fikret Isik Karahanoglu, Nora Leonardi, Zafer Dogan, Jeffrey Adam Kasten, Naghmeh Ghazaleh, Rotem Roza Kopel, Daniela Maria Zöller, Thomas

Silvestro Micera

Short biographical sketch Silvestro Micera received the University degree (Laurea) in Electrical Engineering from the University of Pisa, in 1996, and the Ph.D. degree in Biomedical Engineering from the Scuola Superiore Sant'Anna, in 2000. From 2000 to 2009, he has been an Assistant Professor of BioRobotics at the Scuola Superiore Sant'Anna where he is now the Head of the Neural Engineering group. In 2007 he was a Visiting Scientist at the Massachusetts Institute of Technology, Cambridge, USA with a Fulbri

Official source

Ontological neighbourhood

Statistics

Statistical inference: Statistical hypothesis testing

Related concepts (32)

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 .

False positive rate

In statistics, when performing multiple comparisons, a false positive ratio (also known as fall-out or false alarm ratio) is the probability of falsely rejecting the null hypothesis for a particular test. The false positive rate is calculated as the ratio between the number of negative events wrongly categorized as positive (false positives) and the total number of actual negative events (regardless of classification). The false positive rate (or "false alarm rate") usually refers to the expectancy of the false positive ratio.

Multiple comparisons problem

In statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously or infers a subset of parameters selected based on the observed values. The more inferences are made, the more likely erroneous inferences become. Several statistical techniques have been developed to address that problem, typically by requiring a stricter significance threshold for individual comparisons, so as to compensate for the number of inferences being made.

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 .

False discovery rate

In statistics, the false discovery rate (FDR) is a method of conceptualizing the rate of type I errors in null hypothesis testing when conducting multiple comparisons. FDR-controlling procedures are designed to control the FDR, which is the expected proportion of "discoveries" (rejected null hypotheses) that are false (incorrect rejections of the null). Equivalently, the FDR is the expected ratio of the number of false positive classifications (false discoveries) to the total number of positive classifications (rejections of the null).

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