Compare between t-test and ANOVA.
A t-test is an analysis framework that is used to establish the difference between two sample means from populations that are normally distributed but with unknown variance. It is mostly used when one wants to compare the means of a dependent variable for two independent variables in a normally distributed interval. T-test acts as a hypothesis testing tool that tests assumptions applicable in a population. ANOVA, on the other hand, is a statistical technique that compares the means for more than two populations. It is applied when the researcher wants to conduct tests simultaneously. The variation in any database is split twice; one part is allocated to chance while the other part is allocated to cause. It is less flexible as it can only be generalized to two-tailed t-tests, unlike the t-test which can be one-tailed to the left or right and can also tale the two-tailed test.
Analyzing data in quantitative studies
Data from quantitative studies can be summarized and generalized for a greater population. For this reason, the proper analysis is important so that the results can be generalized to other populations. Data analysis in quantitative studies follows several steps, and the first step is to identify the different levels or scales of measurements. They are nominal, ordinal, ratio and interval and data are grouped as either of the four. The second step is the use of descriptive statistics to make the summary of the data. Visuals are used to identify and classify data in a way that it is easily analyzed. They also help to determine the type of descriptive statistic that should be used. If the data is to be used for making predictions, inferential statistics are employed. Depending on the number of variables in that study, a data analysis method is determined.