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- Proper statistical treatment and presentation of data are crucial for the integrity of any quantitative research paper. Statistical techniques help establish validity, account for errors, test hypotheses, build models and derive meaningful insights from the research.
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Mar 11, 2024 · Proper statistical treatment and presentation of data are crucial for the integrity of any quantitative research paper. Statistical techniques help establish validity, account for errors, test hypotheses, build models and derive meaningful insights from the research.
- Summary
- Introduction to Statistical Treatment in Research
- What Is Statistical Treatment of Data?
- Statistical Treatment Example – Quantitative Research
- Type of Errors
‘Statistical treatment’ is when you apply a statistical method to a data set to draw meaning from it. Statistical treatment can be either descriptive statistics, which describes the relationship between variables in a population, or inferential statistics, which tests a hypothesis by making inferences from the collected data.
Every research student, regardless of whether they are a biologist, computer scientist or psychologist, must have a basic understanding of statistical treatment if their study is to be reliable. This is because designing experiments and collecting data are only a small part of conducting research. The other components, which are often not so well u...
Statistical treatment of data is when you apply some form of statistical method to a data set to transform it from a group of meaningless numbers into meaningful output. Statistical treatment of data involves the use of statistical methods such as: 1. mean, 2. mode, 3. median, 4. regression, 5. conditional probability, 6. sampling, 7. standard devi...
For a statistical treatment of data example, consider a medical study that is investigating the effect of a drug on the human population. As the drug can affect different people in different ways based on parameters such as gender, age and race, the researchers would want to group the data into different subgroups based on these parameters to deter...
A fundamental part of statistical treatment is using statistical methods to identify possible outliers and errors. No matter how careful we are, all experiments are subject to inaccuracies resulting from two types of errors: systematic errors and random errors. Systematic errors are errors associated with either the equipment being used to collect ...
Data processing in research is the process of collecting research data and transforming it into information usable to multiple stakeholders. While data can be looked at in numerous ways and through various lenses, data processing aids in proving or disproving theories, helping make business decisions, or even advancing enhancements in products ...
Statistical methods involved in carrying out a study include planning, designing, collecting data, analysing, drawing meaningful interpretation and reporting of the research findings. The statistical analysis gives meaning to the meaningless numbers, thereby breathing life into a lifeless data.
- Zulfiqar Ali, S Bala Bhaskar
- 10.4103/0019-5049.190623
- 2016
- Indian J Anaesth. 2016 Sep; 60(9): 662-669.
Apr 28, 2020 · This article covers many statistical ideas essential to research statistical analysis. Sample size is explained through the concepts of statistical significance level and power. Variable types and definitions are included to clarify necessities for how the analysis will be interpreted.
Jun 10, 2014 · Statistics is a key tool linking the multiplicity of potential observations of every client with the more abstract concepts of clinical entities, natural histories, clinical response and risks. These more abstract constructs are the foundation on which clinical decisions rest.
This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.
