The first rule of reading a prediction comes from the handbook itself: never assume the predicted number represents the field reliability the user will measure. The models are point estimates regressed from historical data under specific conditions; a National Academies review of the method collects case studies where measured-to-predicted ratios ranged from roughly one-half to twelve, and worse in extremes. The number's power is relative, not absolute — and the handbook says so in as many words.
The questions the numbers answer
Read relatively, the prediction answers real questions with real authority:
| Question | Where to look |
|---|---|
| Does the design meet its allocation? | System λ vs the allocated budget, with margin quantified |
| Where is the reliability going? | The Pareto of contributors — boards, then parts, then factors |
| Which design change is worth it? | Delta between predictions of the current and proposed design |
| Is the thermal design adequate? | πT sensitivity of the hot parts; TJ excursions priced in failures |
| Is the parts policy right? | πQ leverage — what screening buys, what commercial grades cost |
| Where does redundancy pay? | Blocks whose predicted rates break the availability model downstream |
| Which clock is the number on? | Operating-hour vs calendar MTBF; duty cycle and dormancy declared |
| What happens in year one? | Early-life (first-year) estimates where the model set provides them |
The comparison discipline is what makes these answers trustworthy: two predictions compared under the same models, environments, and assumptions cancel most of the handbook's absolute error. That is why the method survived its critics — an obsolete model can still rank two architectures correctly, and ranking is what most programme decisions actually need.
What kind of number you are holding
Two subtleties about what kind of number a prediction hands you. A handbook rate is a point estimate, but not always the same kind: some model families publish rates at a stated upper confidence level, and the newer telecom issues return a mean rate with a standard deviation per part. The distinction bites at roll-up — mean rates add up the tree, but confidence-level rates do not: the 90th-percentile rate of an assembly is not the sum of its parts' 90th percentiles, and a report that sums them anyway is quietly overstating its own confidence. Second, the constant-rate result deliberately says nothing about the first months of service; where infant mortality matters commercially — warranty exposure, launch-year return rates — the telecom-lineage models provide a separate early-life calculation that estimates the elevated first-year behaviour and the credit earned by burn-in. It answers a different question than the steady-state MTBF, and mature reports quote the two side by side without letting either impersonate the other.
| Kind of rate | Behaviour at roll-up |
|---|---|
| Mean rate | Adds up the tree |
| Confidence-level (percentile) rate | Does not add — computed per tree level |
| Early-life / first-year estimate | Separate calculation; quoted beside the steady state, never instead of it |
Margin against the allocation
One number deserves special respect: the margin against allocation. A prediction that lands under its budget is not a curiosity — it is an early warning with time still on the clock. The response is ordered and familiar: attack the Pareto leaders (cooler, softer-driven, better-screened, or fewer parts), renegotiate the allocation across siblings if one block is structurally hard, or escalate to an architecture change while one is still affordable.