Category: Statistics
Alternative to the t-test When Data Are Not Normally Distributed
The t-test is one of the most popular methods for comparing the means of two sample groups. In practice, the t-test can be used for two paired sample groups or two independent sample groups. We can perform this difference test using a paired-samples t-test or an independent-samples t-test, depending on the characteristics of the data being analyzed.
Should Data Normality Testing Always Be Performed in Statistical Analysis?
In statistical analysis of research results, normality testing is often treated as an analytical step that is almost always conducted before proceeding to further analysis. Many researchers, students, and data practitioners believe that without a normality test, statistical analysis results become less scientific.
Differences in Nominal, Ordinal, Interval, and Ratio Data Measurement Scales for Research
In research activities, data serve as the main foundation for analysis and drawing conclusions. However, not all data have the same characteristics. One of the common challenges faced by novice researchers is understanding and applying data measurement scales. In fact, an understanding of data scales greatly determines the type of statistical analysis that can be used as well as the validity of research results.
The Difference Between Residual and Error in Statistics
For those of you who are learning statistics, you’ve probably come across theories explaining the concepts of residual and error. At first glance, they seem almost identical, and many people even think they mean the same thing. However, in statistics, residual and error actually have different meanings.
Understanding Cross-Section, Time Series, and Panel Data Structures in Research
For those of you currently conducting research, I believe it’s important to have a solid understanding of data structure before starting. This is crucial because the structure of your data will determine the appropriate analytical tools to use when analyzing your research results.
Tutorial on One-Way ANOVA Test for Non-Laboratory Research
The one-way ANOVA test is a parametric statistical test used to examine the differences in means across more than two sample groups. It is important to emphasize that the one-way ANOVA is only applicable when comparing three or more groups. If you are comparing the means of only two groups, then a t-test should be used instead.
What Is a Residual Value in Statistics?
If you’re working with data analysis using linear regression, especially the Ordinary Least Squares (OLS) method, it’s important to understand what a residual is. Why does this matter? Because several assumption tests in OLS regression rely heavily on residual values. That’s why you need a solid understanding of what residuals are and how to calculate them.
How to Determine Alpha Values of 5% and 1% in Hypothesis Testing
If you are conducting research, you certainly have a hypothesis for your study. Hypothesis testing is crucial in research, especially if you’re performing inferential statistical analysis. In statistical hypothesis testing, you are often faced with the choice of using an alpha value of 5% or 1% for your study.