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DFFB is also linked to the calibration of the engine fuel-flow meters.
DFFB results can be confirmed by a separate EGT analysis performed by the engine maintenance specialists in the airline.
DFFA is linked to flight conditions. Flight conditions are the main source of error, especially inaccurate aircraft gross weight (payload based on standard weights) and non-negligible FPAC. Therefore, the DFFA value needs to be interpreted with the utmost precaution.
In other words, a high DFFA does not necessarily indicate a high aerodynamic deterioration of the airframe. An altered EPR/thrust relationship versus the reference engine can be responsible for part of the deviation. This is also valid for a brand new engine.
All APM results should be compared to the result of the performance tests carried out during the first flight of the aircraft. This is valid provided the engines on the wings are the same. Some differences can be expected because the first flight of the aircraft is outside normal operational constraints.
Flight Operations & Line Assistance Getting to Grips with Aircraft Performance Monitoring
CRUISE PERFORMANCE ANALYSIS

2.5. The APM statistical analysis
The aim of this paragraph is to give a reminder and some explanations on the way statistics were implemented in the APM program.
2.5.1. General
The APM program features a statistical elimination of measurement points. For the output results DN1/DEPR, DFFA,  DFFB, and DSR, the mean value and the standard deviation are calculated.
Whenever any point of measurement result is outside the 95 % interval of confidence (μ -2 σ, μ + 2 σ) it is eliminated (replaced by a trailing "*") and not included in the relevant parameter mean value and standard deviation.
A low standard deviation value provides a high level of confidence, since it means that all results are consistent and within a limited range.

2.5.2. Mean value (μ)
The simplest statistic is the mean or average. It is easy to calculate an average value and use that value as the "target" to be achieved.
The mean value characterizes the "central tendency" or "location" of the data. Although the average is the value most likely to be observed, many of the actual values are different from the mean. When assessing control materials, it is obvious that technologists will not achieve the mean value each and every time a check is being performed. The values observed would show a dispersion or distribution around the mean, and this distribution would need to be characterized to set a range of acceptable control values.

2.5.3. Standard deviation (σ)
The dispersion of values around the mean value is predictable and can be characterized mathematically through a series of steps, as described below.
1.
The  first mathematical manipulation is to sum () all individual points and calculate the mean or average.
　
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