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Comparing Qlik Analytics and R Software for Accurate Two-Sample T-Tests Research Paper

In this study, we evaluated the accuracy of two-sample Student’s t-tests using Qlik analytics software compared to the gold standard R software. Utilizing data from the Framingham Heart Study, we performed multiple t-tests in both Qlik and R across different scenarios, including variations in sample sizes and p-values. Our findings revealed that Qlik analytics closely match the statistical results of R, making it a reliable tool for performing t-tests.


For the full detailed research paper, please click on the link directly below. To briefly read the Abstract, scroll past the link and read below.


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ABSTRACT

Objective

The statistical determination of a large or small difference between two groups is not based on an absolute standard, but is rather an evaluation of the probability of an event.1,2 In the field of medical research, it is common to use statistical software for descriptive statistics as well as to perform statistical tests.3 However, most software provides ready-to-use functions, and the researchers have almost no information as to how those statistical tests are calculated inside those functions. This article evaluates the accuracy of two-sample Student’s t-test using Qlik analytics software. The gold standard used for this evaluation is the set of standard t-test functions available in R software, a widely used, robust, and reliable statistical software.5–7


Materials and Methods

The tests performed in this evaluation used a subset of Framingham heart study data. The dataset contains data on 4,434 anonymous participants, collected in three periods apart from each other by 6 years from 1956 to 1968. Five t-tests with 2 scenarios each were performed in Qlik analytics and in R and the results compared.


Results

In general, the results for multiple statistics obtained in Qlik analytics match the ones found in R for multiple scenarios: small and large sample sizes, small and large p-values, assuming and not assuming equal variance.


Discussion

Although Qlik analytics matches all statistics for t-test found in R, the p-value only matches up to four decimal points, which is concluded to be enough for testing hypothesis since the conventional levels of significance do not go lower than 0.1.


Conclusion

This research concluded that Qlik analytics can be used for two-sample t-tests in multiple scenarios.


Keywords: Qlik, t-test, r language, Framingham.



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