I found this insightful post on WhatsApp and felt it was worth sharing. It discusses understanding when to apply a statistical test in research, an essential topic for anyone working with data analysis. The post highlights how different types of data and research questions necessitate specific statistical methods to ensure precise and meaningful outcomes. It further explains various statistical tests, their applications, and the process of selecting the appropriate one. Whether you’re an experienced researcher or just beginning, this guide offers valuable information to help you master the complexities of statistical testing. Here it is:
T-test
Use it when you want to see if there is a big difference between the average scores of two groups of students: one group taught in the usual way and another group taught in a new way.
ANOVA (Analysis of Variance)
Use this when you want to check if three or more groups have different averages. For example, you want to see if students from different schools have different average scores. It helps you find out if the differences are important.
Regression (Simple and Multiple)
Use this when you want to look at how one thing depends on one or more other things. For example, you want to see how hours studied affect exam scores (simple regression), or how hours studied, exam scores, and student motivation are connected (multiple regression). This helps you understand their relationships better.
Chi-squared test
Use this when you want to see if there is a significant link between two categorical things. For example, you want to find out if there is a significant link between smoking and lung cancer. This helps you understand if the two are related.
Wilcoxon rank-sum test (Mann-Whitney U test)
Use this when you want to compare the distributions of two separate groups. For example, you want to see if there is a difference in scores between students who had traditional teaching and those who had innovative teaching. It helps you understand if their score distributions are different.
Kruskal-Wallis H test
Use this when you want to compare the distributions of three or more separate groups. For example, you want to see if scores are different among students from different schools. This helps you understand if their score distributions vary.
Friedman test
This is used when you want to compare how scores change over different times for a group. Example: You want to see how students’ scores change over several testing sessions.
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Pearson correlation coefficient
Use this when you want to look at how two things that can change smoothly are connected. Example: You want to see how hours spent studying are connected to exam scores.
Spearman rank correlation coefficient
Use this when you want to study how two variables are related, especially when the data does not follow a normal pattern. Example: You want to explore how people rank their favorite foods compared to how they rank their perceived nutritional value.
Kendall’s tau correlation coefficient
Use this when you want to study how two things are connected, especially when they are in categories or ranks. Example: You want to see how people’s social status relates to their level of education.
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