Details

Autor(en) / Beteiligte

• Yadav, Anil Kumar
• Kartikeyan, M
• Raman, Rohan Kumar
• Das, Basant Kumar
• Jha, Dharm Nath
• Sahu, Sanjeev Kumar

Titel

Chi-Square Test of Significance

Ist Teil von

• Concept Building in Fisheries Data Analysis, 2022, p.81-94

Ort / Verlag

Singapore: Springer

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Test of significance provides an objective procedure for distinguishing between whether the observed difference signifies any real difference among groups. It indicates whether observed differences between assessment results occur because of sampling error or chance. The experiments in fisheries science are affected by a substantial amount of uncontrolled variations making such tests necessary. Sometimes data are best collected or conveyed nominally or categorically. These data are represented by counting the number of times a particular event or condition occurs. There are many instances in inland fisheries research, wherein nominal/categorical data describe the phenomenon under investigations more adequately than interval/ratio data. Chi-square, a non-parametric test of significance, is an appropriate test when the data are in the form of frequency counts occurring in two or more mutually exclusive categories (nominal variables). It enables us to decide on the basis of sample if (1) a given set of counts (or frequencies) statistically match some known, or expected, set or (2) two or more categories are statistically independent. In this article, test of significance based on chi-square is presented with the examples on inland fisheries data. The data used in the article are analysed using MS Excel/SPSS.