Search results
Results From The WOW.Com Content Network
In many laboratories, it is the original place of record of data (no copying is carried out from other notes) as well as any observations or insights. For data recorded by other means (e.g., on a computer), the lab notebook will record that the data was obtained and the identification of the data set will be given in the notebook. [4]
The GAISE document provides a two-dimensional framework, [11] specifying four components used in statistical problem solving (formulating questions, collecting data, analyzing data, and interpreting results) and three levels of conceptual understanding through which a student should progress (Levels A, B, and C). [12]
However, certain conditions must be met before the replication of the experiment is commenced: the original research question has been published in a peer-reviewed journal or widely cited, the researcher is independent of the original experiment, the researcher must first try to replicate the original findings using the original data, and the ...
Similarly, for a regression analysis, an analyst would report the coefficient of determination (R 2) and the model equation instead of the model's p-value. However, proponents of estimation statistics warn against reporting only a few numbers. Rather, it is advised to analyze and present data using data visualization.
Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data." [3]
Experimental data in science and engineering is data produced by a measurement, test method, experimental design or quasi-experimental design. In clinical research any data produced are the result of a clinical trial. Experimental data may be qualitative or quantitative, each being appropriate for different investigations.
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
MIAME describes the minimum information required to ensure that microarray data can be easily interpreted and that results derived from its analysis can be independently verified. [2] There are a number of file formats used to represent this data, as well as both public and subscription-based repositories for such experiments. [2]