Statistical Methods for Research Workers is a classic book on statistics, written by the statistician R. A. Fisher. It is considered by some to be one of the 20th century's most influential books on statistical methods, together with his The Design of Experiments (1935). It was originally published in 1925, by Oliver & Boyd (Edinburgh); the final and posthumous 14th edition was published in 1970.
According to Denis Conniffe:
Ronald A. Fisher was "interested in application and in the popularization
of statistical methods and his early book Statistical Methods for Research Workers, published in 1925, went through many editions and
motivated and influenced the practical use of statistics in many fields of
study. His Design of Experiments (1935) [promoted] statistical technique and application. In that book he
emphasized examples and how to design experiments systematically from
a statistical point of view. The mathematical justification of the methods
described was not stressed and, indeed, proofs were often barely sketched
or omitted altogether ..., a fact which led H. B. Mann to fill the gaps with a rigorous mathematical treatment in his well-known treatise, ."
Prefaces
Introduction
Diagrams
Distributions
Tests of Goodness of Fit, Independence and Homogeneity; with table of χ2
Tests of Significance of Means, Difference of Means, and Regression Coefficients
The Correlation Coefficient
Intraclass Correlations and the Analysis of Variance
Further Applications of the Analysis of Variance
SOURCES USED FOR DATA AND METHODS INDEX
In the second edition of 1928 a chapter 9 was added: The Principles of Statistical Estimation.
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ce cours s'inscrit dans une offre de cours interdisciplinaires et collaboratifs ouverts aux étudiant·e·s de l'UNIL et de l'EPFL.
Il s'oriente principalement vers la connaissance de l'histoire de Lausa
This course covers topics in applied biostatistics, with an emphasis on practical aspects of data analysis using R statistical software. Topics include types of studies and their design and analysis,
Statistical Methods for Research Workers is a classic book on statistics, written by the statistician R. A. Fisher. It is considered by some to be one of the 20th century's most influential books on statistical methods, together with his The Design of Experiments (1935). It was originally published in 1925, by Oliver & Boyd (Edinburgh); the final and posthumous 14th edition was published in 1970. According to Denis Conniffe: Ronald A.
In null-hypothesis significance testing, the p-value is the probability of obtaining test results at least as extreme as the result actually observed, under the assumption that the null hypothesis is correct. A very small p-value means that such an extreme observed outcome would be very unlikely under the null hypothesis. Even though reporting p-values of statistical tests is common practice in academic publications of many quantitative fields, misinterpretation and misuse of p-values is widespread and has been a major topic in mathematics and metascience.
In scientific research, the null hypothesis (often denoted H0) is the claim that no relationship exists between two sets of data or variables being analyzed. The null hypothesis is that any experimentally observed difference is due to chance alone, and an underlying causative relationship does not exist, hence the term "null". In addition to the null hypothesis, an alternative hypothesis is also developed, which claims that a relationship does exist between two variables.
Delves into the spatial energy spectrum in turbulence theory, crucial for analyzing energy distribution between spatial scales and its connection to measurable quantities.
Discusses methods for identifying differentially expressed genes in genomic data analysis.
Explores the estimation of R in multiple testing scenarios and the challenges of P-value calculations.