Compare the Testing Group Differences using T-tests, ANOVA, and Nonparametric TESTS | Statswork

Compare​ ​the​ ​Testing​ ​Group​ ​Differences​ ​using​ ​T-tests​,​ ​ANOVA​,​ ​and Nonparametric​ ​TESTS​​ ​|​ ​Statswork The main purpose of this blog is to understand the Testing Group Differences using​ ​T-tests​,​ ​ANOVA​,​ ​and​ ​Nonparametric​​ ​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​:

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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 ● Friedman test – Compare mean rank between three or more related groups ● Kruskal-Wallis test – Compare the mean rank between two or more distinct​ ​groups ● Spearman’s​ ​rank​ ​correlation​ ​–​ ​Relationship​ ​between​ ​two​ ​variables.

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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.

Table​ ​1.​ ​Comparison​ ​between​ ​the​ ​T-test​ ​and​ ​One-way ANOVA ​ ​ ​ ​ ​ ​T-test

LHS (Dependent)

RHS (Independent)

One-way​ ​ANOVA

Interval​ ​or​ ​ratio variable

Interval​ ​or​ ​ratio variable

Binary​ ​variable​ ​with only​ ​two​ ​groups

Categorical​ ​variable (mORE​ ​THAN​ ​2 GROUPS)

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Null Hypothesis

µ1​ ​=​ ​µ​ ​2

µ1​ ​=​ ​µ​ ​2​ ​=​ ​µ​ ​3​ ​=…

Prob.

Distribution

T​ ​distribution

F​ ​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.

©​ ​2017-2018​ ​All​ ​Rights​ ​Reserved,​ ​No​ ​part​ ​of​ ​this​ ​document​ ​should​ ​be​ ​modified/used​ ​without​ ​prior​ ​consent Statswork​ ​™​ ​ ​-​ ​ ​www.statswork.com INDIA:​ ​Nungambakkam,​ ​Chennai​ ​–​ ​600​ ​034 ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​UK:​ ​The​ ​Portergate,​ ​Ecclesall​ ​Road,​ ​Sheffield,​ ​S11​ ​8NX