Statistical Tools for Data Analysis
In any type of research, statistical analysis is essential to properly represent your data results. Statistics is a part of the research process that answers questions through data. To perform statistical analysis, you must properly collect and organize data. Depending on your data set and the questions you need to answer, there are several types of statistical data analysis to choose from.
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ANOVA
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ANOVA or analysis of variation is a statistical tool used to test the hypothesis of an experiment. ANOVA tests allow the researcher to determine if one factor has a significant effect on other factors in the research study. It does this by calculating a p-value. If the p-value is less than 0.05, there is a significant difference between the two factors. Statistical software such as Excel Data Analysis, Minitab and Sigma Stat calculate ANOVA of a given data set for you. In addition, there are different variations of ANOVA tests, such as single factors, two factors with replication and two factors without replication that are available for your use, depending on the type of data set you have.
Correlation Analysis
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The Correlation analysis statistical tool measures if two different variables correlate together. For instance, if you are studying the weight and height of an animal, you would use the two sets of data and calculate the correlation coefficient to determine if there is a correlation between weight and height. In other words, does weight increase as height increases. The correlation coefficient will be expressed between negative one and positive one. Being closer to either negative one or positive one results in a positive or negative correlation. This statistical tool is available in statistical programs such as Excel, Minitab and Sigma Plot.
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Regression
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The Regression data analysis tool conducts a linear regression using the "least squares" method. This tools fits a line in a graph through a given set of data. The output line allows you to analyze how a single dependent variable is affected by other variables. Regression analysis can be performed using various statistical software programs.
Student T-tests
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Student t-tests examine the equality of the data mean or average of each sample in the study. The t-test analysis results in illustrating if the means of the data are significantly different from each other. The t-test results in a p-value. If the p-value is less than 0.05, the results indicate that the means are significantly different. There are different types of t-tests depending on the data set. For example, there is a two-sample assuming equal variance t-test and a two-sample assuming unequal variances t-test in which two data sets came from distributions with the same variances or different variances, respectively. Student t-tests are also available in various statistical software programs.
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