Quantitative analysis consists of performing analysis on quantitative data with the help of several statistical techniques. Quantitative analysis generally involves statistical techniques like significance testing, regression analysis, multivariate analysis, etc. Analysis on quantitative data, called Quantitative analysis, is mainly done by those people who have an in-depth knowledge of statistical techniques that are used to perform Quantitative analysis. Quantitative analysis is mainly done in order to draw a statistical inference about the data under consideration or under study. Quantitative analysis is basically carried out by the researcher at that time when he wants to predict, understand, and interpret the data in a statistical manner in order to get a clear picture about the population under study.
Quantitative analysis is mainly classified into two categories: Estimation and Testing of the hypothesis.
In Quantitative analysis, the estimation part involves the ideal properties of the estimator that are used while estimating the data. In Quantitative analysis, the estimator is said to be an ideal estimator if it possesses any one of the properties of the ideal estimators. The properties of the ideal estimator in Quantitative analysis are unbiasedness, consistency, efficiency and sufficiency.
The unbiasedness property in Quantitative analysis is basically a kind of property that states that the estimator needs to give unbiased results to be considered an unbiased estimator. If an estimator gives a parameter with some constant as an estimate, then that estimator would not be considered an unbiased estimator.
A sufficient estimator is obtained by the researcher in Quantitative analysis with the help of a criterion called Fisher-Neyman Factorization criterion. This criterion in quantitative analysis would be appropriate for the convenient characterization of a sufficient estimator.
The second category of Quantitative analysis includes the tests of hypothesis. The concept of the testing of hypothesis in Quantitative analysis is mainly based on the testing of the null and alternative hypothesis. The null hypothesis in Quantitative analysis is an assertion that states that there is no statistical difference between the two samples under consideration. On the other hand, the alternative hypothesis in Quantitative analysis is the complement of the null hypothesis.
An important aspect of Quantitative analysis is that the researcher can also commit errors while computing Quantitative analysis. The errors that are conducted by the researcher in Quantitative analysis are divided into two categories, namely Type I error and the Type II error.
Type I error is the one that involves rejection of the correct sample during Quantitative analysis.
On the other hand, Type II error is the one that involves acceptance of an incorrect or false sample during Quantitative analysis.
In the field of medical / nursing, committing Type II error during Quantitative analysis is seriously dangerous. According to the definition of the Type II error in Quantitative analysis, if the researcher accepts a defective drug, then this can pose a serious health hazard problem.
In the field of psychology, quantitative techniques, like statistical significant tests like t-test, f-test, z-test, chi square test, etc. are used. Suppose one wants to compare the literacy rate in region A to the literacy rate in region B. After conducting a primary research over a given sample drawn from the region, Quantitative analysis will be followed. For this case, Quantitative analysis in the form of a right tailed t-test will be performed. This is called a right tailed test because in this case, the alternative hypothesis is LRA>LRB in Quantitative analysis. A t-test statistic in Quantitative analysis is obtained, and if the calculated value is more than the tabulated value at the given level of significance, then the null hypothesis will be rejected. Otherwise it will be accepted.
