In the good olden days, manual computation was used to validate the data related to medical research. This gave way to computational error based on human inaccuracy or mistake. Then the cost involved in the research was large pertaining to a large number of values, say over 1000 fields to observe. But with gradual technological advancements, statistical tools have been developed to help in medical research. In this guest post let's discuss the statistical tools developed.

As far as medical research is concerned, clinical trials, exactness, precision, meta-analysis, reviews are all very important. Apart from these, the parameters that validate the research are most important. The most critical thing in testing is data, assumptions in the research are base on 100% correct ess of the data. Connecting all the dots a student who is pushing courses related to medical research needs to submit an assignment based on these above-mentioned points. For their easiness they can come to hire experts of BookMyEssay, to get Medical Science Assignment Help.

Further, the data received for research is not distributed evenly. Thus, it becomes important to set aside a portion of 1% for error and the result will have an error. Yes, this probability is very low and the error approaches 0% as the benchmark increases.

Process of Data Analysis

There are a number of variables in the data like numeric values, alphanumeric values, strings, points, etc. Though all this looks confusing and intimidating, the process of analyzing the data is not complex. Let's look at the 3 step process of data analysis:

1. Origin of the Data: It is important to find out and understand the basis of the origin of data. Also, you should know the circumstances in which the data was recovered.
2. Estimation Process to be Used: Now that the data has been receive, we need to make a choice of the process to analyze the data. Actually, at this step, we find out what to do with the data.
3. Interpreting the Result: Finally comes the stage which relates the data to the solution. Consequently, it means giving sense to the data.

Statistical Tools Used in the Medical Research

The universe of statistical tools used for medical research is vast. Again, there are different types of tools but with differences. The basic dissimilarity between these tools is the ease to use, licensing, cost and presentation. What do the tools do? They steer through end to end process. Again the processes are collecting data, organizing it, analyzation of data, and finally interpreting it. Whenever the students are given assignments on medical research they can use these steps to frame their report. They can get Science Assignment Help on hire through professional teams BookMyEssay.

Let's Look At Some of the Statistical Tools:

1. Stata: It is the most important analytical tool present for medical research. The word Stata comes from two words statistics and data. It was first released in 1985, furthermore, its graphical user interface came into use in 2003. Stata is commonly used for international organizations like United Nations, government organizations, academicians, public, social and economic works. Besides being a complex toolbox it provides a colorful graphical interface and data analysis and management capability.
2. R: The first version was released in 1993 while the integrated development environment (IDE) came into being in 2011. R is an open-source software tool. It is equipped to handle, visualize and analyze. Further, it's a new type of interface for users and the programming is a "command-line interface" (CLI). Again, this software tool has been used by a strong base of users which stretches up to 6000 packages. R is supported by a long list of data scientists, medical researchers, bioinformatics. Moreover, it covers analysis for clinical data, cancer data, phylogeny, molecular biology, etc.
3. GraphPad Prism: The best part of this software is that it comes with a built-in page that interprets the result of the data after the estimation and analysis we did. Moreover, the language used here is less technical and easy to understand. Biologists, academicians, people from industry especially like this tool.
4. SAS: SAS is the abbreviation for Statistical Analysis System. It has functionalities that work across scientific and engineering organizations. The first stone for SAS was laid in 1966 by Anthony Bar of North Carolina State University. Moreover, SAS has a compatibility level with Excel files, SPSS files, ext files, Stata, JML, XML, etc. A further advantage of this tool is that data can be transferred to and from without resorting to a manual process that may cause errors.
5. IBM SPSS: The first version of SPSS was developed in 1968, then it was acquired by IBM in 2009. It's a go-to tool for most professions and disciplines. It is easy to use, moreover it has a graphical user interface. However, the catch in using this tool is that the researchers should have prior knowledge of data elements like identification, measuring, case selection, etc. It is compatible with Excel files, TXT files, SAS, Stata.

Conclusion

Statistical analysis has to be done very carefully by the data analysts. There can be errors and mistakes in data collection, analysis, or choice of type of test. Thus hiring experienced analysts always saves time and energy. Students studying medical research can contact BookMyEssay for Medical Science Assignment Help.