In above example, one-way ANOVA test is used to compare the means when DBP follows normal distribution while Kruskal-Wallis H tests/median tests are used to compare the distribution of DBP among three age groups when DBP follows non-normal distribution. In case of non-normal DBP variable, median value is our representative measure and null hypothesis stated that distribution of the DB P values among three age groups are statistically equal. If our DBP variable is normally distributed, mean value is our representative measure and null hypothesis stated that mean DB P values of the three age groups are statistically equal. Suppose we want to compare the diastolic blood pressure (DBP) between three age groups (years) (50). In the inferential statistics, hypothesis is constructed using these measures and further in the hypothesis testing, these measures are used to compare between/among the groups to calculate significance level. Similarly in the categorical data, proportion (percentage) while for the ranking/ordinal data, mean ranks are our representative measure. If continuous variable follows normal distribution, mean is the representative measure while for non-normal data, median is considered as the most appropriate representative measure of the data set. The choice of the most appropriate representative measure for continuous variable is dependent on how the values are distributed. For example, in the regression analysis, when our outcome variable is categorical, logistic regression while for the continuous variable, linear regression model is used. For the nominal, ordinal, discrete data, we use nonparametric methods while for continuous data, parametric methods as well as nonparametric methods are used. Two main statistical methods are used in data analysis: descriptive statistics, which summarizes data using indexes such as mean, median, standard deviation and another is inferential statistics, which draws conclusions from data using statistical tests such as student's t-test, ANOVA test, etc.įor the same objective, selection of the statistical test is varying as per data types.
STATISTICAL TOOLS FOR DATA ANALYSIS PPT SOFTWARE
Incorrect statistical methods can be seen in many conditions like use of unpaired t-test on paired data or use of parametric test for the data which does not follow the normal distribution, etc., At present, many statistical software like SPSS, R, Stata, and SAS are available and using these softwares, one can easily perform the statistical analysis but selection of appropriate statistical test is still a difficult task for the biomedical researchers especially those with nonstatistical background. Practice of wrong or inappropriate statistical method is a common phenomenon in the published articles in biomedical research. Other than knowledge of the statistical methods, another very important aspect is nature and type of the data collected and objective of the study because as per objective, corresponding statistical methods are selected which are suitable on given data. To select the appropriate statistical method, one need to know the assumption and conditions of the statistical methods, so that proper statistical method can be selected for data analysis. In statistics, for each specific situation, statistical methods are available to analysis and interpretation of the data. A wrong selection of the statistical method not only creates some serious problem during the interpretation of the findings but also affects the conclusion of the study. Selection of appropriate statistical method is very important step in analysis of biomedical data.
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STATISTICAL TOOLS FOR DATA ANALYSIS PPT HOW TO
In the present article, we have discussed the parametric and non-parametric methods, their assumptions, and how to select appropriate statistical methods for analysis and interpretation of the biomedical data.
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All type of statistical methods that are used to compare the means are called parametric while statistical methods used to compare other than means (ex-median/mean ranks/proportions) are called nonparametric methods. Selection of appropriate statistical method depends on the following three things: Aim and objective of the study, Type and distribution of the data used, and Nature of the observations (paired/unpaired). Two main statistical methods are used in data analysis: descriptive statistics, which summarizes data using indexes such as mean and median and another is inferential statistics, which draw conclusions from data using statistical tests such as student's t-test. In biostatistics, for each of the specific situation, statistical methods are available for analysis and interpretation of the data.