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The 21st century aviation and aerospace technologies have evolved and become more complex and technical. Turbofan jet engines as well as their cousins, the rocket engines (liquid/solid) have gone through several design upgrades and enhancements during the course of their design and exploitation. These technological upgrades have made engines very complex and expensive machines which need constant monitoring during their working phase. As the demand and use of such engines is growing steadily, both in the civilian and military sectors, it becomes necessary to monitor and predict the behavior of parametric data generated by these complex engines during their working phases. In this paper flight parameters such as Exhaust Gas Temperature (EGT), Engine Fan Speeds (N1 and N2), Fuel Flow (FF), Oil Temperature (OT), Oil Pressure (OP), Vibration and others where used to determine engine fault. All turbo fan engines go through several distinctly different working phases: Take-off phase, Cruise phase and Landing phase. Recording generated parametric data during these different phases leads to a massive amount of in-flight data and maintenance reports, which makes the task of designing and developing a fault diagnostic system highly challenging. It becomes imperative to use modern techniques in data analysis that can handle large volumes of generated data and provide clear visual results for determining the technical status of the engine under investigation/monitoring. These modern techniques should be able to give clear and objective assessment of the object under investigation. Cluster analysis methods based on Neural Networks such as c-means, k-means, self-organizing maps and DBSCAN algorithm have been used to build clusters. Differences in cluster groupings/patterns between healthy engine and engine with degraded performance are compared and used as the bases for defining faults. Fault diagnosis plays a crucial role in aircraft engine management. Timely and accurate detection of faults is the foundation on which maintenance turnaround times, operational costs and flight safety are based. The data used in this paper for analysis was obtained from flight data recorder during one flight cycle. The final decision on a fault is taken by an engineer.

Biomedical research relies on proving (or disproving) a research hypothesis, and P value becomes a cornerstone of “null hypothesis significance testing.” P value is the maximum probability of getting the observed outcome by chance. For a statistical test to achieve significance, the error by chance must be less than 5%. The pros are the P value that gives the strength of evidence against the null hypothesis. We can reject a null hypothesis depending on a small P value. However, the value of P is a function of sample size. When the sample size is large, the P value is destined to be small or “significant.” P value is condemned by one school of thought who claims that focusing more on P value undermines the generalizability and reproducibility of research. For such a situation, presently, the scientific world is inclined in knowing the effect size, confidence interval, and the descriptive statistics; thus, researchers need to highlight them along with the P value. In spite of all the criticism, it needs to be understood that P value carries paramount importance in “precise” understanding of the estimation of the difference calculated by “null hypothesis significance testing.” Choosing the correct test for assessing the significance of the difference is profoundly important. The choice can be arrived by asking oneself three questions, namely, the type of data, whether the data is paired or not, and on the number of study groups (two or more). It is worth mentioning that association between variables, agreement between assessments, time-trend cannot be arrived by calculating the P value alone but needs to highlight the correlation and regression coefficients, odds ratio, relative risk, etc.