Quantitative statistical analysis involves applying mathematical procedures to specific data, with apps available for the process. The two general groups are interval estimates and hypothesis tests, with the former requiring parameters set on a specific data sample. The latter focuses on research and defining a series of hypotheses. The process can be lengthy, with companies using shorter methods for timely decision-making.
Quantitative statistical analysis is any mathematical procedure that individuals apply to specific data. There is no shortage of apps for this scanning process. Investors can use this type of statistical analysis to evaluate stocks, and researchers define hypotheses and companies evaluate key decisions using this process. The two general groups of the quantitative analysis process are interval estimates and hypothesis tests, which provide specific tools for your use.
Interval estimates require parameters set on a specific data sample. This process begins with the selection of a sample from a larger population pool, since it is often impractical to measure an entire population. In this type of statistical analysis, population is a broad term that represents any large data set. From the selected sample, individuals and companies can make inferences about the larger population set. Each sample must be large enough to make these inferences.
Once individuals have a sample, they need to find out what kinds of statistics apply to the data. For example, descriptive statistics are among the most common for quantitative statistical analysis. These statistics include the mode, mean, and median along with the standard deviation and variance, among other potential statistics. The application of confidence levels is also included here. People and companies often seek to achieve the highest level of confidence possible in order to make accurate inferences.
The second broad group of quantitative statistical analysis, hypothesis testing, focuses more on research than practical business application. Researchers often look at a topic or situation and define a series of hypotheses. The purpose of the applied statistical techniques is to support or not support each hypothesis. In some research reports, interval estimates or other quantitative methods may be included.
Most research cases have a null hypothesis and an alternative hypothesis. In quantitative statistical analysis, the null hypothesis tends to mean that things are the same as before or that two items are the same. The alternative hypothesis indicates that there are some changes from the initial null hypothesis. The significance level defines how strong the support for the analysis is or is not. A critical region represents values at which a researcher can reject the null hypothesis.
Quantitative statistical analysis is often a lengthy process. Companies tend to use shorter methods to provide timely data for decision making. In other words, not all available statistical tools serve a purpose in these studies. Investigative reports often require multiple tools due to the length, depth, and breadth of the report. The type of report or need for information dictates the tools needed for the process.
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