Statistical Tests
Introduction Analysis of Variance (ANOVA): An OverviewWhat is a One-Way ANOVA and What is it Used For?One-Way ANOVA ExampleWhat is a Two-Way ANOVA and What is it Used For?Two-Way ANOVA ExampleWhat is the difference between a one-way and two-way ANOVA?Summary of the Differences between One-way and Two-way ANOVA Introduction This article provides a comprehensive overview of the two main types of Analysis of Variance (ANOVA) tests: one-way ANOVA and two-way ANOVA. It begins by explaining the general concept of ANOVA and its widespread use in various fields to compare means across groups. The article details when each type of ANOVA...
A One-way ANOVA, also called a one-way analysis of variance is a statistical method mainly used to test whether there are significant differences in means when there are 3 or more groups. In other words, it is an extension of the independent sample t test, when the independent groups are more than 2. In this article, we present a detailed discussion of the one-way ANOVA, when to use it, one-way anova assumptions, examples and a step-by-step process of calculating a one-way ANOVA by hand. However, if you are familiar with the one-way ANOVA and wishes to know how to perform...
IntroductionParametric Test: Definition and AssumptionsNon-parametric Test: Definition and CharacteristicsKey Differences Between Parametric and Non-parametric TestsExamples of parametric tests and Their Non-Parametric Test EquivalentsFinal Take AwayFAQsWhat is the advantage of using nonparametric tests?What is the advantage of using parametric tests? What central tendency values are considered for parametric and non-parametric tests?What are examples of parametric tests?What are examples of nonparametric tests? Introduction Parametric and non-parametric tests are two fundamental concepts in statistical analysis. Understanding the difference between these two types of tests is crucial for researchers and analysts in selecting the appropriate statistical method for their data analysis. Parametric tests assume...
The Chi-Square Test is a way to figure out if there’s a significant connection between two categories in a set of data. It checks if the categories are independent, making it a powerful tool for data analysis. Definition The Chi-Square Test of Independence is a crucial tool for statisticians. It helps determine if there's a meaningful connection between two categories in a set of data. In simpler terms, it checks if changes in one category can affect another. Key Notes About Chi-Square Test of Independence The Chi-Square Test looks at the relationship between two categories. It needs the data to...
The Chi-square goodness of fit test is a non-parametric statistical test used to determine the extent to which observed values deviate from expected values. This test enables the comparison of an observed sample distribution with an expected probability distribution, assessing how well theoretical distributions such as normal, binomial, or Poisson fit the empirical data. By dividing sample data into intervals and comparing the number of data points falling into each interval with the expected values, one can ascertain the goodness of fit. Procedure for Conducting the Chi-Square Goodness of Fit Test: Formulating the Hypotheses Null Hypothesis: The null hypothesis for...