Top 11 Statistical Tools for Effective Data Research | iCert Global

Many sectors rely on usual evidence and proof generation based on data. The evidence-based practice era relies primarily on researchers' ability to report out on findings. Stats have always been involved in other decisions made by leaders and executives when making decisions. Statistically working and visually displaying data can appear to be overwhelming.

Here is a list of the common statistical tools:

1. R Considering R if you are going to work with data - this is one of the best tools you can use as R is an open-source statistics option that is free! Every researcher employing statistics uses R and the number of productive tools available to store and process your data is great! Although it takes a while to learn R is a power use your data statistically, but processing takes time.

 2. Python -Python owns a versatile language which has increased its use in analyses and stats especially as it relates to research and analysis. Python is useful to bring together statically programming and the other experts that collects and analyzes text, organizes tests, analyzes photos and so forth.

3. GraphPad Prism - GraphPad Prism is an excellent tool to do scientific charts, graphs, fitting curves, and make stats easier to use. Prism has many different analyses, t-tests, comparisons, study tables, survival, etc.

4. SPSS (Statistical Package for the Social Sciences) - SPSS is a very common tool to study human behavior. SPSS is a straightforward program to use, because of its clean interface.

5. SAS (Statistical Analysis System) -  SAS is a very good statistical analysis tool. SAS is a very powerful program, as used in healthcare, as well as business. SAS allows you to complete advanced statistics as well as graphs and charts.

6. Stata - Stata is a strong technology tool as well, one that social science researchers, researchers in economics, biology, and political science also use.

7. Minitab - Minitab is a good option because it is usable by both novices and experts. You can perform all kinds of statistical analysis and produce a variety of graphs, including histograms and scatter plots.

8. Excel - Excel can be good for statistical analysis too, but is better for doing simple data work. Excel does not independently perform statistical operations, but you can depict data and use simple stats with it.

9. MATLAB - MATLAB is a programming language/tool often used by engineers and scientists. MATLAB allows users to program code to solve specific problems. It allows the user to customize their work as needed, and gives them flexibility.

10. JMP - JMP is an analysis tool developed by scientists and engineers with the intent to aid users in the processing of data and uncovering key patterns.

11. Tableau - Tableau is an analysis tool that is used to turn data into visually appealing views. Tableau is well established in data dashboards, connecting large amounts of data effectively while creating a simple visual for the user.

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Conclusion

Having good knowledge of all the essential statistical tools is central to carrying out good research and obtaining reliable results and valid interpretations. The tools you select can help you avoid misleading conclusions and good decision-making. If you want to learn more about becoming a data analytics expert consider taking an iCert Global course or certification.

FAQs

1. What are the common uses for statistical tools?

Some methods in research use statistics and statistical tools to support claims, help consolidate and make sense from large datasets, help visually represent a complex data set or pull a lot of things together quickly.

2. Are there free statistical tools?

There are many commercial and open-source statistical analysis tools available. Some free tools include TIMi Suite, MiniTab, Grapher, XLSTAT, NumXL, Posit, Qualtrics DesignXM, and SAS Viya.

3. Can I use Tableau to perform statistical analysis?

Tableau is essentially a statistical data analysis tool that has been developed so that anyone can use it to perform the analysis of statistical data.

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