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It is known that the essence of statistical significance is to determine whether there is a factual basis for a difference between indicators selected for analysis. According to Tenny and Abdelgawad (2022), statistical significance in a study is defined as a certain measure of the probability of the truth of the null hypothesis in comparison with the acceptable level of uncertainty about the proper answer. This paper’s primary focus is the definition and justification of statistical significance in the research context.
A “Statistically Significant” Finding
Generally, when researchers perceive a finding as “statistically significant,” they are more likely to imply a real and reliable discovery rather than a random and spontaneous one. In order to confirm these judgments, as a rule, it is required to use the process of null hypothesis significance testing. In this case, statistical significance reveals the correctness of the assumptions and the probability of their results. It allows one to choose among the presented theories, leading to excellent practice results.
An Example of a Set of Data
Based on the study by Kaul et al., it becomes evident that chromatin contacts become a statistically significant data set. Accordingly, the authors emphasize important chromatin contacts in the context of Fit-Hi-C (Kaul et al., 2020). Hence, they seek to identify them regarding calculating statistical reliability estimates for Hi-C contact cards. The authors especially consider and evaluate whether the event occurs as a result of chance, which is one of the reasons this study was chosen. They are trying to understand whether the result is statistically significant, which in turn means that it is unlikely to be obtained due to random events or fluctuations.
Conclusion
Summarizing the above, it is necessary to state that to effectively implement changes, more is needed to rely on the results of A/B testing. They may show random results that are not true. For this purpose, statistical significance is used — a quantitative indicator that the study results are not unexpected and can be recognized as reliable. The most striking example of the application of this concept can be seen in the example of the study by Kaul et al.
References
Kaul, A., Bhattacharyya, S., & Ay, F. (2020). Identifying statistically significant chromatin contacts from Hi-C data with FitHiC2. Nature Protocols, 15(3), 991-1012. Web.
Tenny, S., & Abdelgawad, I. (2022). Statistical significance. StatPearls Publishing, Treasure Island (FL).
What does it mean for research to be statistically significant? (n.d.). CloudResearch. Web.
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