Statistical methods in the context of Statistician

Metascience (also known as meta-research) is the use of scientific methodology to study science itself. Metascience seeks to increase the quality of scientific research and enhance its efficiency. It is also known as "research on research" and "the science of science", as it uses research methods to study how research is done and find where improvements can be made. Metascience concerns itself with all fields of research and has been described as "a bird's eye view of science". In the words of John Ioannidis, "Science is the best thing that has happened to human beings ... but we can do it better."

In 1966, an early meta-research paper examined the statistical methods of 295 papers published in ten high-profile medical journals. It found that "in almost 73% of the reports read ... conclusions were drawn when the justification for these conclusions was invalid." Meta-research in the following decades found many methodological flaws, inefficiencies, and poor practices in research across numerous scientific fields. Many scientific studies could not be reproduced, particularly in medicine and the soft sciences. The term "replication crisis" was coined in the early 2010s as part of a growing awareness of the problem.

Economic history is the study of history using methodological tools from economics or with a special attention to economic phenomena. Research is conducted using a combination of historical methods, statistical methods and the application of economic theory to historical situations and institutions. The field can encompass a wide variety of topics, including equality, finance, technology, labour, and business. It emphasizes historicizing the economy itself, analyzing it as a dynamic entity and attempting to provide insights into the way it is structured and conceived.

Using both quantitative data and qualitative sources, economic historians emphasize understanding the historical context in which major economic events take place. They often focus on the institutional dynamics of systems of production, labor, and capital, as well as the economy's impact on society, culture, and language. Scholars of the discipline may approach their analysis from the perspective of different schools of economic thought, such as mainstream economics, Austrian economics, Marxian economics, the Chicago school of economics, and Keynesian economics.

Computational statistics, or statistical computing, is the study which is the intersection of statistics and computer science, and refers to the statistical methods that are enabled by using computational methods. It is the area of computational science (or scientific computing) specific to the mathematical science of statistics. This area is fast developing. The view that the broader concept of computing must be taught as part of general statistical education is gaining momentum.

As in traditional statistics the goal is to transform raw data into knowledge, but the focus lies on computer intensive statistical methods, such as cases with very large sample size and non-homogeneous data sets.

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. The impulse to write a book on the statistical methodology he had developed came not from Fisher himself but from D. Ward Cutler, one of the two editors of a series of "Biological Monographs and Manuals" being published by Oliver and Boyd.

Quantitative analysis in finance refers to the application of mathematical and statistical methods to problems in financial markets and investment management. Professionals in this field are known as quantitative analysts or quants.

Quants typically specialize in areas such as derivative structuring and pricing, risk management, portfolio management, and other finance-related activities. The role is analogous to that of specialists in industrial mathematics working in non-financial industries.

Population study is an interdisciplinary field of scientific study that uses various statistical methods and models to analyse, determine, address, and predict population challenges and trends from data collected through various data collection methods such as population census, registration method, sampling, and some other systems of data sources. In the various fields of healthcare, a population study is a study of a group of individuals taken from the general population who share a common characteristic, such as age, sex, or health condition. This group may be studied for different reasons, such as their response to a drug or risk of getting a disease.

 This article incorporates public domain material from Dictionary of Cancer Terms. U.S. National Cancer Institute.

Analysis of variance (ANOVA) is a family of statistical methods used to compare the means of two or more groups by analyzing variance. Specifically, ANOVA compares the amount of variation between the group means to the amount of variation within each group. If the between-group variation is substantially larger than the within-group variation, it suggests that the group means are likely different. This comparison is done using an F-test. The underlying principle of ANOVA is based on the law of total variance, which states that the total variance in a dataset can be broken down into components attributable to different sources. In the case of ANOVA, these sources are the variation between groups and the variation within groups.

ANOVA was developed by the statistician Ronald Fisher. In its simplest form, it provides a statistical test of whether two or more population means are equal, and therefore generalizes the t-test beyond two means.