PMP Pass Probability Calculator
The formula
What the pmp odds tells you
The Project Management Professional exam model: 180 items, achievement-level scoring across People/Process/Business domains.
Calculation: Logistic on mock+hours; ~60–65 % pass-rate anchored. Enter your inputs and every substitution is shown below the result.
Reading the result
The ECO domains shifted toward agile; study plans built only on predictive ITTOs now under-serve half the paper.
Rules of thumb: Pass estimates hover near 60–70 %
Limitations and best practice
Answering with your company’s reality instead of PMI’s ideal — the exam wants the textbook.
This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Frequently asked questions
Above/below per domain?
Validity?
Source & methodology
PMI Examination Content Outline; community data.
PMP Pass Probability: what this tool answers
Every bet is a price, and this page computes whether the price is fair. Searchers land on a PMP Pass Probability page for three reasons — to plan, to compare options, and to check that somebody else’s arithmetic is honest. This tool serves all three, and everything after the calculator explains the model instead of hiding it.
People rarely disagree about arithmetic; they disagree about assumptions that were never written down. Printing the substitution and the sensitivity ranking removes that category of disagreement for PMP Pass Probability.
What makes PMP Pass Probability worth calculating is not precision but position: it tells you which side of a threshold you are on, and by how much. A rough figure that settles the direction beats an elaborate one that never commits.
What makes a calculation page trustworthy is that its copy is derived from its own arithmetic: here pass probability = 76.2%, recomputed every time a field changes, and the sensitivity section ranks the inputs that move the result — Mock-exam % correct first, at ≈39.2 % of the headline for a ten-percent error either way in that input. Read the sections in order and you will be able to reproduce every figure by hand.
The PMP Pass Probability formula, explained
Here is the complete model this tool runs — Logistic on mock+hours; ~60–65 % pass-rate anchored — with each input explained below. If a competitor’s page gives you a different answer from the same inputs, this is the text that settles which of you is right.
Mock-exam % correct (example value 72) is a measured number in the unit named by the label; the formula does not convert for you. Rates are the most mis-transcribed input on any page like this; copy it from the document rather than retyping from memory.
Study hours logged (example value 60) is a quantity rather than a category — precision here is cheap and worth having. If the period is partial, decide whether you mean elapsed or planned time and keep that meaning across every field.
PMP Pass Probability with real numbers, step by step
Abstract formulas are hard to trust, so here is the complete substitution using the default inputs, written the way an examiner would want it. Starting figures: Mock-exam % correct, Study hours logged. The endpoint of that chain is 76.2% of pass probability, sitting in the “PMI publishes achievement thresholds instead of raw lines — this profile (72 % mock, 60 hrs) projects 76 %; candidates above 70 % mock consistently clear it” band.
- Mock term (72−65)/5.5
- Hours term (60−70)/90
- Logistic → 76 %
Items — 180. Read next to the headline, this is the figure that shows the shape of the trade-off.
Time per item — 1.28 min. Compare it against the main result of 76.2% before drawing conclusions.
Domain risk — Agile/hybrid now ~half the exam. If your inputs change, this is usually the first companion figure to move.
Audit note — random audits: log hours as you go. Compare it against the main result of 76.2% before drawing conclusions.
Overwrite any input above and every line of this example updates with it — the substitution is live, not a screenshot of somebody else’s numbers.
The PMP Pass Probability corridor: your result across a realistic band
Read this table as the practical error bar on the headline figure. It answers the question people actually have after seeing a single number: how wrong could this be if the main input is off by ten or twenty percent?
| Mock-exam % correct setting | Pass probability |
|---|---|
| 80 % of the example value | 18.9% |
| 90 % of the example value | 46.3% |
| example inputs | 76.2% |
| 110 % of the example value | 92.2% |
| 120 % of the example value | 97.8% |
Across 5 settings of Mock-exam % correct, the result spans 18.9% to 97.8%. A decision that only works at one row of this table is a decision waiting for an input to move. Reproduce it in the form above by editing Mock-exam % correct alone; the substitution steps will follow your figures exactly.
The levers behind PMP Pass Probability, ranked by effect
Nobody should have to guess which input matters. The engine was re-run once per field below — ten percent up, ten percent down, everything else frozen — and the results ranked by how far they moved the pass probability. For PMP Pass Probability the ranking is measured rather than guessed, and it belongs to these defaults: shift the starting point and the order can shift with it.
- Mock-exam % correct +10 % → PMP Pass Probability = 92.2% · −10 % → 46.3% (the pass probability shifts ≈39.2 %)
- Study hours logged: ten percent up gives 77.4%, ten percent down gives 74.9% — ≈1.6 % of the pass probability either way
Mock-exam % correct is the dominant lever here: ten percent either way moves the result by about 39.2 %, from 46.3% to 92.2%. That is where a careless figure does the most damage, so check it against a source, and treat Study hours logged as the second priority (≈1.6 % per ten percent). The rest of the inputs earn only a rounding-error of attention, and that asymmetry is the practical payoff of measuring rather than asserting.
What a PMP Pass Probability of 76.2% means in practice
Nobody acts on a bare figure — they act on a figure with a comparison attached. On the example inputs the band reads “PMI publishes achievement thresholds instead of raw lines — this profile (72 % mock, 60 hrs) projects 76 %; candidates above 70 % mock consistently clear it”; your own figures place you on a different rung of the same ladder. The sensitivity list shows how far a modest input error moves you along that ladder: for Mock-exam % correct, ±10 % covers 46.3% to 92.2%. The practical reading rules: ask what would have to change to move you one band. If the answer is “a small, plausible input difference”, the band itself is not the finding — the uncertainty is.
Related measures answer related questions, and the distinctions matter: PMP Pass Probability is the quantity, Pass probability is this tool’s reading of it, and the decision that follows belongs to neither of them separately.
Why PMP Pass Probability results go wrong — and how to prevent it
Answering with your company’s reality instead of PMI’s ideal — the exam wants the textbook. This calculator gives an estimate based on standard formulas; it is not professional financial, medical, legal or engineering advice. Verify anything you plan to act on with a qualified professional and your own records.
Reusing a PMP Pass Probability from last season. In Education & Academics the inputs are the thing that drifts; the formula does not.
Attention is also misallocated between fields. Having measured this engine, we can say where it belongs: Mock-exam % correct first, everything else second.
Another is comparing across contexts: a PMP Pass Probability computed with one set of conventions should never be lined up against a figure computed with another. Check the inputs behind both before ranking them.
A quick check for any PMP Pass Probability figure
- Record the date and the inputs alongside the number so future-you knows what was assumed.
- Cross-check any figure used in an application against the official source for the same cycle.
- Check Mock-exam % correct before anything else: it is the measured dominant input, ≈39.2 % of PMP Pass Probability per ten percent.
- Note the date and the inputs beside the number so the figure can be audited later.
- State, in one sentence, which convention each rate, unit or period in the form is using.
- Confirm the answer against one independent method or published benchmark for PMP Pass Probability.
Step by step: getting a trustworthy PMP Pass Probability out of this tool
The tool does the arithmetic; the routine below is what makes the arithmetic trustworthy. Five steps, a minute total — and afterwards you will know not just what the number is, but which assumption produced it.
Step 1 — one document, one pass. Open the statement, label or record that holds mock-exam % correct, study hours logged and transcribe straight into the form. Transcription from a single source removes the mismatch errors that appear when figures are assembled from three different places. The inputs are the part of the work that nobody else can do for you.
Step 2 — enter the conservative case first. Resist the flattering scenario. Type the inputs you would defend in front of a sceptical reader, read the Pass probability (76.2% for the example values), and note the verdict band. When in doubt, prefer the boring input: the interesting one is usually the one you misread.
Step 3 — measure what deserves measuring. The ranked levers above tell you where a careful figure pays off. Everything else can stay approximate, which is a licence to stop hunting for data you do not need. If the verdict changes with a ten-percent nudge, report the nudge alongside the number.
Step 4 — run the scenario trio. Expected, pessimistic, optimistic. The gap between pessimistic and expected is the number to plan against; the optimistic result is useful only as a ceiling. Note the assumption you are least sure about; that is where the real risk lives.
Step 5 — write the one-line memo. “Pass probability = 76.2% on this date, from these inputs.” That line is the entire deliverable; everything else on this page is how you earned the right to write it. When two sources disagree, it is almost never the arithmetic that differs.
Where PMP Pass Probability matters in practice
Most Standardized Tests disagreements are arithmetic disagreements in disguise. When Pass probability of 76.2% sits on the table with its working shown, the conversation stops being about who sounds more confident and starts being about which input is wrong. When in doubt, prefer the boring input: the interesting one is usually the one you misread.
The companion outputs — Items and Time per item — exist for the second stage of that screen: the headline sizes the prize, while the companions describe the trade-off you are accepting to get it. Say the answer out loud before you save it; if it sounds wrong, the model did not change, your understanding did.
There is a personal-administration use people overlook: periodic self-checking. PMP Pass Probability quietly drifts as life changes — rates move, bodies change, costs compound — and re-running it a few times a year catches drift while correction is still cheap. When two sources disagree, it is almost never the arithmetic that differs.
PMP Pass Probability — frequently asked questions
What affects PMP Pass Probability the most?
PMP Pass Probability is driven above all by Mock-exam % correct. Perturbing only that field by ten percent either way produced 92.2% against the 76.2% baseline — a ≈39.2 % response, while Study hours logged manages ≈1.6 %.
Which matters more: study hours logged or mock-exam % correct?
Mock-exam % correct, by a factor of about 24.2. Concretely: ten percent on Mock-exam % correct is worth ≈39.2 % of PMP Pass Probability, the same nudge on Study hours logged only ≈1.6 %. That ranking is re-computed for each page from that page’s own defaults.
What is a typical PMP Pass Probability?
With the example inputs on this page the result is 76.2%, labelled “PMI publishes achievement thresholds instead of raw lines — this profile (72 % mock, 60 hrs) projects 76 %; candidates above 70 % mock consistently clear it”. “Typical” varies with situation, which is why every result here ships with its verdict band and its sensitivity list: they turn one figure into a range you can locate yourself inside.
How do I calculate PMP Pass Probability by hand?
Three steps: write the formula (Logistic on mock+hours; ~60–65 % pass-rate anchored), insert your values, and simplify. The worked example section shows exactly that chain for the default inputs; replicating it once on paper teaches you more about PMP Pass Probability than any number of calculator runs.
What happens to PMP Pass Probability if mock-exam % correct changes by 10 %?
Try it in the form above and the printed substitution will follow you; the pre-computed answer for the defaults is 46.3% to 92.2% across the ten-percent probe, i.e. ≈39.2 % sensitivity on the pass probability.
Is the PMP Pass Probability result of 76.2% good or bad?
Neither, on its own — the label “PMI publishes achievement thresholds instead of raw lines — this profile (72 % mock, 60 hrs) projects 76 %; candidates above 70 % mock consistently clear it” is the band this figure falls in for the example inputs, and your own inputs will place you elsewhere on the same ladder. Judge the number against your history and your alternatives, and remember that a ten-percent error in Mock-exam % correct alone moves it by ≈39.2 %.
What affects PMP Pass Probability the most?
Sensitivity depends on where you start, but the inputs Mock-exam % correct and Study hours logged typically move PMP Pass Probability further than the rest because they enter the formula multiplicatively. If you can only measure two things well, measure those and hold the others at conservative defaults.
How is PMP Pass Probability weighted?
Every component’s weight sits in the formula: Logistic on mock+hours; ~60–65 % pass-rate anchored. If a component feels under-weighted for your situation, edit that input and watch PMP Pass Probability move — the tool is a sandbox for exactly that question.
Above/below per domain?
Yes — you need “Above Target” in most; Near Target can pass.
Validity?
Three-year cycle to recertify with PDUs.
Can this tool see the numbers I type?
It cannot. There is no submission step: the calculation happens in your browser and the result disappears when you close the page. Treat the fields above as a scratchpad rather than a form.
What the labels on this page mean
- Items
- 180 on the example inputs — a companion figure printed beside the headline of PMP Pass Probability so that no result is read in isolation.
- Time per item
- 1.28 min in the default PMP Pass Probability calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Domain risk
- Agile/hybrid now ~half the exam in the default PMP Pass Probability calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Pass probability
- The primary output of this tool: 76.2% for the default inputs, computed exactly from Logistic on mock+hours; ~60–65 % pass-rate anchored.
- Worked example
- The step-by-step substitution printed above, so the arithmetic can be re-derived by hand or in a spreadsheet.
- Verdict band
- A named zone on the range of plausible results; the example inputs land in “PMI publishes achievement thresholds instead of raw lines — this profile (72 % mock, 60 hrs) projects 76 %; candidates above 70 % mock consistently clear it”, and your inputs may not.
- Sensitivity (±10 %)
- How far the PMP Pass Probability moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
The provenance of this PMP Pass Probability calculation
The model is published arithmetic implemented exactly as written: Logistic on mock+hours; ~60–65 % pass-rate anchored. Source of record: PMI Examination Content Outline; community data. Where a professional version of this calculation differs, it differs in inputs and jurisdiction-specific coefficients, not in the algebra — which is why re-checking your inputs beats hunting for a second calculator.
Treat the output as a precise answer to the question you typed: exact arithmetic, transparent formula, published source, no hidden rounding. The promise to the reader is narrow and testable: same inputs, same output, no silent rounding, and no figure presented without the formula that produced it.
After the number: what to do with it
Compare the computed edge against the actual price being offered; a positive theoretical edge still loses to bad odds.
The audience here is players deciding whether a wager is mathematically worth taking. Everything you need to defend the result — formula, substitution, sensitivity, source — is printed above, so the only remaining variable is the honesty of your inputs.