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Sep 8, 2020 · Statistical treatment of data involves the use of statistical methods such as: mean, mode, median, regression, conditional probability, sampling, standard deviation and. distribution range.
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.
- Write your hypotheses and plan your research design. To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.
- Collect data from a sample. In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying.
- Summarize your data with descriptive statistics. Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarize them.
- Test hypotheses or make estimates with inferential statistics. A number that describes a sample is called a statistic, while a number describing a population is called a parameter.
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.
- Descriptive Statistics. Descriptive statistics are used to describe the overall characteristics of a dataset. This includes measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation).
- Inferential Statistics. Inferential statistics are used to make predictions or inferences about a population based on a sample. This is done by using estimation methods (point estimates and confidence intervals) and testing methods (hypothesis testing).
- Regression Analysis. Regression analysis is a method used to identify the relationships between different variables. For example, you could use regression analysis to understand how customer satisfaction ratings change based on the number of support tickets they open.
- Correlation analysis. Correlation analysis is a method used to understand how two variables relate. For example, you could use correlation analysis to understand how customer satisfaction ratings change based on the number of support tickets they open.
Statistical treatment of data is an important aspect of all experimentation today and a thorough understanding is necessary to conduct the right experiments with the right inferences from the data obtained.
Statistical treatment can mean a few different things: In Data Analysis : Applying any statistical method — like regression or calculating a mean — to data. In Factor Analysis : Any combination of factor levels is called a treatment.
