It is vital for researchers to select an appropriate analytical method when analyzing research data. Some researchers use paired test for unpaired data or use parametric statistical test for data that does not follow the normal distribution. At the same time, although there is various statistical software which simplifies the procedure of the statistical tests, the selection of an appropriate analytical method is still a problem.
Generally, before choosing a correct analysis, researchers must figure out the following four aspects:
CD BioSciences’ experts are experienced in analyzing large scale and complex data. We can help our clients to identify analytical methods which better fit their data or answer their question/hypothesis more accurately.
We can not only provide consulting services on the selection of analytical method, but also analyze client's data using following statistical approaches.
Meta-analyses typically combine two or more independent trials (often with different designs and conducted for different reasons) into the same analytic framework. In a conceptually similar study, statistical methods are used for meta-analysis to arrive at common truths. In addition to providing estimates of unknown general truths, the meta-analysis is able to compare the results of different studies and determine the patterns and the sources of divergence between these results, or other interesting relationships that may be exposed in multiple researches.
Decision tree is a very common classification method. It is a decision analysis method based on the known probability of various situations, which is used to calculate the probability of net present value greater than or equal to zero by forming a decision tree, to evaluate the risk of one project and to judge its feasibility.
Cluster analysis is a method of studying objects in a particular clustering in mathematical statistics, that is, a group of known individuals will be divided into several types according to indicators (variables) before analyzing.
During the data processing, there are often two or more different test results. When comparing and analyzing the data, we should not make conclusion merely based on the difference between the two results, but on the statistical analysis and the test of significance of difference.
Non-parametric methods analysis is an important part of statistical analysis methods, which is the basic content of statistical inference together with parameter test. The parameter test is a method to infer the parameters such as mean, variance, etc. of the general distribution in the case of a known general distribution.
In statistics, a regression model is a mathematical model for quantitatively describing statistical relationships. Among them, linear regression is a fixed-effect statistical analysis method that determines the quantitative relationship between two or more variables.
We are also capable of dealing data with other methods such as parametric, logistic and linear regression, non-inferiority and superiority, permutation and simulation.
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