Non Parametric Tests and Research Methodology
This document covers the following topics:
- Non Parametric Tests: Wilcoxon Rank Sum Test, Mann-Whitney U test, Kruskal-Wallis test, Friedman Test
- Introduction to Research: Need for research, Need for design of Experiments, Experiential Design Technique, Plagiarism
- Graphs: Histogram, Pie Chart, Cubic Graph, Response Surface Plot, Counter Plot Graph
- Designing the Methodology: Sample size determination and Power of a study, Report writing and presentation of data, Protocol, Cohorts studies, Observational studies, Experimental studies, Designing clinical trial, Various phases
Download the PDF, notes, and PPT for detailed information on these topics.
Detailed Explanation of Non Parametric Tests and Research Methodology
Non-parametric tests are statistical methods used when the data does not meet the assumptions required for parametric tests. These tests are particularly useful when dealing with non-normal distributions or ordinal data. Some of the key non-parametric tests include:
- Wilcoxon Rank Sum Test: Used to compare two independent samples to determine if they come from the same distribution.
- Mann-Whitney U Test: Another test for comparing two independent samples, often used as an alternative to the t-test.
- Kruskal-Wallis Test: A non-parametric alternative to the one-way ANOVA, used for comparing more than two independent samples.
- Friedman Test: Used to detect differences in treatments across multiple test attempts, often used for repeated measures.
Research methodology is a crucial aspect of any study, ensuring that the research is conducted systematically and scientifically. Key components include:
- Need for Research: Understanding the importance of research in advancing knowledge and solving real-world problems.
- Design of Experiments: Planning and designing experiments to ensure valid and reliable results.
- Experiential Design Technique: Techniques for designing experiments that account for variability and control confounding factors.
- Plagiarism: Understanding the ethical implications of plagiarism and how to avoid it.
Graphs are essential tools for visualizing data and interpreting results. Common types of graphs include:
- Histogram: A graphical representation of the distribution of numerical data.
- Pie Chart: A circular statistical graphic that is divided into slices to illustrate numerical proportions.
- Cubic Graph: A three-dimensional graph used to represent complex data sets.
- Response Surface Plot: A graphical representation of the relationship between multiple variables and a response variable.
- Counter Plot Graph: A graph that uses contour lines to represent three-dimensional data on a two-dimensional surface.
Designing the methodology involves several critical steps, including:
- Sample Size Determination: Calculating the appropriate sample size to ensure the study has sufficient power to detect an effect.
- Power of a Study: The probability that the study will detect an effect if there is one.
- Report Writing and Presentation of Data: Techniques for effectively communicating research findings.
- Protocol: A detailed plan of the research study, including objectives, methodology, and statistical analysis.
- Cohorts Studies: Longitudinal studies that follow a group of people over time.
- Observational Studies: Studies where the researcher observes the effect of a risk factor without intervening.
- Experimental Studies: Studies where the researcher manipulates one or more variables to observe the effect.
- Designing Clinical Trials: Planning and conducting clinical trials to evaluate the effectiveness of medical interventions.
- Various Phases: Understanding the different phases of clinical trials, from initial safety testing to post-marketing surveillance.
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