Compare The Testing Group Differences Using T-Tests, ANOVA And Non-Parametric Tests

The main purpose of this blog is to understand the Testing Group Differences using T-tests, ANOVA, and Non parametric Measures.

 Choosing the right test for your data analysis is a very difficult task particularly identifying the Different methods from testing group differences is the Biggest challenging task. It is important to have to in-depth Knowledge to understand and calculate T-tests, ANOVA, and Nonparametric, brief interpretation of the output. In order to choose the right statistical test, when analyzing the data from an experiment, we must have a good understanding of some basic statistical terms and concepts:


Test for Normality

Every data must follow certain distribution. But we have to find the appropriate distribution from goodness of fit test. So, our data is checked through each and every distribution. Hence, goodness of fit test is very tedious. This way of estimation of data is called by parametric tests. Parametric tests always give the reliable estimated value.

 If the data follow the normal distribution, then we can use parametric statistical tests. According to the central limit theorem, if the sample size is large, all data must follow the normal distribution.

List of parametric tests and their usage?

  • Independent sample t test – Compare means between two groups.
  • Paired sample t test – Compare means between related groups.
  • ANOVA – Compare the means between two or more distinct groups.
  • Pearson correlation coefficient – Relationship between two variables.

List of non-parametric tests and their usage?

  • Mann-Whitney U test – Compare mean rank between two groups.
  • Kruskal-Wallis test – Compare the mean rank between two or more distinct groups.
  • Spearman’s rank correlation – Relationship between two variables.
  • Comparing Group Means: The T-test and One-way ANOVA Using STATA, SAS, and SPSS
While the t-test is inadequate to comparing means of two groups, one-way ANOVA can compare more than two groups. Therefore, the t-test is considered a special case of one-way ANOVA. These analyses do not, however, necessarily imply any causality (i.e., a causal relationship between the left-hand and right-hand side variables).

Table 1:

Compares the t-test and one-way ANOVA:

T-TestOne-Way ANOVA
LHS (Dependent)Interval or ratio variableInterval or ratio variable
RHS (Independent)Binary variable with only two groupsCategorical variable (MORE THAN 2 GROUPS)
Null Hypothesisµ1 = µ 2µ1 = µ 2 = µ 3
Prob. DistributionT distributionF distribution

Writing a research paper for statistical related paper play a critical role when we are testing group differences using T-tests, ANOVA, and Nonparametric TEST. Choosing the right statistical guidance for your Comparing Group Means will help to complete your research paper as soon as possible.
Location: United Kingdom

Comments

Post a Comment

Popular posts from this blog

Simple Data Analysis Techniques, Top 5

Image
Reacquaint yourself with simple yet important data analysis tools to transition from abstract numbers to quantifiable success. In today’s digital era, we are inundated with information—it’s overwhelming. Leaders at the top helm make decisive decisions daily. Sorting through avalanche of data to assist management’s decision making is the key. Objective is to sort and  interpret  and report findings. Toward this end, tools or techniques for statistical data analyses achieve this objective. Buzzwords we hear today are big data, hadoop, and AI; however, rudimentary tools need to be deployed to their maximum capability, which is not the case in most firms. Propel your  data analysis   program by mastering the following 5 basic techniques. Review these mundane statistical techniques, yet so vital. Techniques Merits Demerits 1. Hypothesis Testing Hypothesis tests are used in multiple disciplines such as science, research, economics, etc. Popularly known as t-testing, h
Read more

Approaching Data Analysis: How To Interpret Data? – Beginners Guide

Image
With more and more companies using big data, demand for professional data analysts has witnessed exponential growth in the recent years. “Learners of data analysis and interpretation need to have an in-depth understanding of the subject along with the statistical acumen and working knowledge of tool sets to make significant progress in the field of data analytics” – Dr. Venkatesh, Statistician. To gain good expertise in Data analysis and interpretation, the beginner learner requires good foundation in basic statistics, and better skills of how to input, process and analyze data by employing a range of data analytical and visualization tools. The objectives of data analysis and interpretation are to manipulate the data, both qualitative as well as quantitative, to obtain utilizable information that is critical to making informed decisions. It involves the usage of many different methods and procedures that can, Describe and summarize data Evaluate and enhance qua
Read more

Statswork Systematic Review Vs Meta-Analysis

Image
Systematic review and meta-analysis are often confused and used interchangeably albeit having distinct differences between them. Both are reviews of high quality evidence that form a part of literature review.  What is a Systematic review?  A systemic review seeks to find answer to a defined question by collecting and synthesizing high quality evidence from different studies. It is an exhaustive summary of all scientific evidence substantiated to a particular question. Systematic reviews came to be as a way to improve the quality of literature review. Systematic review applies to all types of research, both qualitative and quantitative. Systematic review evolved in the health care industry and clinical trials, but has since found its application in variety of fields like social sciences, astronomy, etc. What is a Meta-analysis?   Meta-analysis integrates statistically similar studies using statistical principles to achieve an estimate of common point of truth b
Read more