Statistical analysis is a method of analyzing data to determine patterns and trends and to determine the statistical significance. It includes five steps: identifying the relevant factors to be studied; calculation of suitable statistical statistic; measurement of statistical outcome; interpretation of statistical results and conclusions based on those results; and communication of statistical results. There are several ways to analyze statistical data. The methods may include random sampling, mathematical statistics, maximum likelihood estimation and binomial tree. Some techniques may be used in combination to provide better accuracy and precision.

For large multivariate studies, it is usually necessary to combine several statistical methods to obtain a statistically significant result. This can be done using least squares (LS) and multiple factor (MFS) statistical analysis. In a nutshell, LS is an application in which the mean-square value is determined and then compared with a standard mean from the other variables being analyzed. Multiple factor statistical analysis on the other hand involves two variables, which are normally analyzed separately, and a comparison is made between them. For this type of statistical analysis, the range that is to be measured is dependent on the type of variable, which needs to be analyzed.

Non-parametric statistical methods are based on continuous variables and their mean values over time. They assume that the variance is linearly correlated between the variables. For example, the Student’s t-test and chi-square test are non-parametric. A Student’s t-test is based on two variables, while a chi-square test is based on one variable only. Another example is Student’s t-plot analysis, which is also a non-parametric way of analyzing continuous data sets. The assumption here is that the variance is linearly correlated between the continuous variables.

On the other hand, the types of statistical tests that are often used for testing whether a result is significant are the t-tests, the chi-square test, the one-sided test, and the multivariate test. When a set of data sets is analyzed using at least one of these techniques, a significant result will be discovered. However, there is one major problem with this type of analysis: all results are statistically significant only when the hypothesis testing is performed properly.

Most researchers perform hypothesis testing in two different ways. The most commonly used method is called the null hypothesis test. In this type of statistical analysis, there is no actual difference in the means of the predictor variable and the dependent variable, which is a non-parametric form. The null hypothesis simply states that there is no significant difference between the outcome variable and the reference variable.

The chi-square test, or more formally known as the chi-squares, is another common method of testing for significant results. In this procedure, there are normally six degrees of freedom, referred to as the levels of variation. These levels are the variance of the mean, the variance of the distribution, and the standard deviation, which are simply the deviation of random variables from their mean value. There are more degrees of freedom than in the t-test and, therefore, there will be more significant results. However, it is important to note that just because a significant result is obtained in the chi-square does not necessarily mean that the result is significant when tested using the Student’s t-test or the one-way analysis of variance. Because chi-squares do not normally vary much from their mean values, they provide little insight into the underlying meaning.

The Bonferroni method, named after its creator G. Bonferroni, is a widely used procedure for testing the significance of results with chi-squares. This type of analysis relies on the fact that the random variables can be compared using the means of their corresponding extreme values. This procedure is known to produce significant results when a significant difference exists between the means, but when testing for a significance relation, Bonferroni’s rule may be applied. As may be seen by the example just presented, if there is a fifteen percent significant difference between the means of the distributions of salaries, the Bonferroni rule can be used to interpret this data in order to determine whether the mean value is significantly different from zero. Because this procedure produces significant results, however, it is often applied in conjunction with other tests in order to eliminate extreme values.

Another popular statistical test that is used in many fields of study is the t statistics, which is sometimes also called chi-square testing. This type of statistical analysis requires the use of a two-sided Chi-square chart where one side represents the set of data that is to be tested and analyzed, and the other side the null distribution against which the data is to be compared. Because the degrees of freedom associated with this type of statistical analysis are relatively high, significant results can often be obtained using this procedure.