Fin Maverick
Foundations VocabularyAccounting & ReportingEconomics & MacroQuant Methods & ProgrammingBusiness & Company AnalysisCorporate Finance & ValuationBehavioural Finance
Banking & Market InfrastructureFixed Income & RatesDerivatives & Structured ProductsPublic EquitiesTransactions & DealsPortfolio ConstructionFunds & AMCs
Private Markets & AlternativesRisk, Treasury & ControlAI & Digital FinanceStochastic Calculus & PricingWealth & Personal FinanceIndian Markets & RegulationProfessional Practice
CalculatorComparison
Frameworks
Explore Bootcamps
Equity ResearchPortfolio ManagementMutual Fund MasteryFinancial LiteracyInvestment Banking Analyst
Private Equity AnalystHedge Funds AnalystBreaking Into VCBreaking Into QuantsAI For Finance
Financial Analyst ProgramRisk Management ProgramPrivate Wealth ManagementDebt Capital MarketsDerivatives Foundation
Explore Internships
Equity Research InternMutual Fund Intern
Portfolio Management InternFinancial Literacy Intern
Explore Micro Courses

Equity Research6

Writing an Investment ThesisBuilding a Discounted Cash FlowReading an Annual Report FastReading a Sector Before a CompanySpotting Quality of Earnings Red FlagsBuilding a Revenue Forecast From Drivers

Portfolio Management3

Rebalancing: When, Why and What It CostsStrategic and Tactical Asset AllocationMeasuring Risk in a Portfolio

Mutual Fund Mastery3

Comparing Funds Without Being FooledHow a NAV Is Struck and Which Day You GetReading a Fund Factsheet Properly

Derivatives Unlocked4

Hedging a Real ExposureThe Greeks, PracticallyFutures, the Basis and What Moves ItReading an Option Payoff

AI For Finance2

Retrieval and Grounding for FinanceDocument Extraction in Finance

Breaking Into Quants4

Backtesting a StrategyHypothesis TestingCleaning Financial DataRegression for Finance

Breaking Into VC3

Sizing a MarketReading a Term Sheet as a FounderHow a Venture Round Actually Works

Financial Analyst Program4

Common Size and Trend AnalysisReading a Cash Flow StatementRatio Analysis That Says SomethingBuilding a Working Capital Schedule

Risk Management Program2

Credit Exposure and How It Is ReducedValue at Risk and What It Hides

Investment Banking Analyst3

Precedent Transactions and Why They DifferReading a Term Sheet StructurallyBuilding a Comparable Companies Table

Private Wealth Management3

Tax Aware Portfolio DecisionsBuilding a Client Risk ProfileGoal Based Planning Arithmetic

Debt Capital Markets3

Analysing an Issuer's CreditDuration and What It Does Not Tell YouBond Pricing and Yield Mechanics

Private Equity Analyst2

Fund Waterfalls and CarryThe LBO in Structure

Hedge Funds Analyst2

Short Selling MechanicsLong Short Mechanics
Courses
Explore Career Roadmaps
Investment Banking AnalystEquity Research AnalystVC AnalystPrivate Equity AnalystHedge Funds Analyst
Quant AnalystAI For FinanceFinancial Analyst ProgramPrivate Wealth ManagementDebt Capital Markets
Risk Management ProgramDerivatives FoundationPortfolio ManagementMutual Fund Mastery
PartnershipsShowdown
Log inSign up
Quant Analyst · CoreTrack
1Quantitative Methods, Financial Data & Programming
iProbability
Probability in FinanceRandom VariableProbability DistributionsThe Normal DistributionNormal Distribution ProbabilityThe Lognormal DistributionRandomness vs Uncertainty
iiStatistics and Inference
Population and SampleMean, Median and ModePrecision and AccuracyVariable TypesVariance, Standard Deviation and…Dispersion MeasuresStatistical BiasEffect SizeHypothesis TestingThe Sampling DistributionSkewnessKurtosisCovarianceConfidence IntervalArithmetic Mean vs Geometric MeanStatistical Significance vs Economic…Confidence Interval vs Prediction IntervalHow to Summarise a…
iiiCorrelation and Regression
RegressionCorrelation and CausationOrdinary Least SquaresInteraction TermsRegression CoefficientsRegression vs ClassificationHow to Build a…Spurious CorrelationRegression, Correlation and FitResidualsMulticollinearityAutocorrelation and Partial Autocorrelation
ivTime Series
Time Series in FinanceSimple, Weighted and Exponential…Moving Average CalculatorPrice, Return and Level SeriesHow to Prepare Time-Series…LagFrequencySeasonalityTimestampsTrendStationarity and the Unit RootHeteroskedasticityLeadRolling WindowsDifferencing
vSimulation and Numerical Methods
SimulationMonte Carlo SimulationHow to Run a…Numerical MethodsIterationResampling and the BootstrapPseudorandom Numbers and the SeedConvergence and ToleranceNumerical Stability
viOptimisation
OptimisationLocal and Global OptimaConstraintsConvex OptimisationThe SolverLinear ProgrammingThe Objective FunctionConstraint ViolationThe Feasible SetLagrange MultipliersQuadratic Programming
viiModelling Practice
Linear, Logistic, Ridge and…Training, Validation and Test…The ModelModel ErrorDependent and Independent VariablesThe ROC Curve and AUCWhat a Model HoldsMSE, RMSE, MAE and MAPEPrecision and RecallCross Validation and RegularisationOverfitting and UnderfittingReturn Series MeasuresSimple, Compound and Log Return
viiiBacktesting and Research Integrity
BacktestingBacktest vs Live PerformanceHow to Document a…How to Prevent Backtest…Out-of-Sample TestingWalk-Forward AnalysisMultiple TestingP-HackingData Snooping
ixData Quality and Structure
Data QualityThe DatasetSelection and Survivorship BiasVersioned DatasetsData Structures in FinanceData CleaningMissing Data and Null ValuesStructured Data vs Unstructured DataMissing Data vs ZeroData Validation vs Data CleaningOutliersDuplicate Records
xProgramming for Finance
Data PipelinesAPIs for Financial DataAPI vs CSV FileDatabases in FinancePython for FinanceJoinsSQL for FinanceThe Analysis Workflow
xiQuantitative Research
Research DesignThe Data Generating ProcessReproducibilityPeer Review in Analytical WorkThe Research HypothesisRobustness and Sensitivity
2Stochastic Calculus & Derivative Pricing Theory
iProbability Foundations
The Probability SpaceRandom VectorsSigma-AlgebraExpectationSample Space and EventsDensity and Distribution FunctionsRisk-Neutral ProbabilityState Price Density vs…
iiStochastic Processes and Jumps
Properties of a Stochastic ProcessMartingaleBrownian Motion and Its PropertiesBrownian Motion vs Geometric…Stopping TimeThe Markov PropertyState VariablesTransition ProbabilityQuadratic VariationQuadratic Variation vs Ordinary…Submartingale and SupermartingaleMartingale RepresentationMarkov Process vs MartingaleOptional StoppingFiltrationJump ProcessesThe Poisson ProcessLevy ProcessesJump Diffusion
iiiIto Calculus
The Ito IntegralThe Ito Integral vs the Riemann IntegralInfinitesimals in Stochastic CalculusQuadratic CovariationIto's LemmaHow to Apply Ito's…The Infinitesimal GeneratorIto Calculus vs Ordinary Calculus
ivStochastic Differential Equations
Stochastic Differential EquationsStochastic Differential Equation vs…Drift and DiffusionStrong and Weak Solutions ComparedDiscretisationGeometric Brownian Motion
vPricing Theory and No-Arbitrage
No-ArbitrageGirsanov, Radon-Nikodym and Change…Physical and Risk-Neutral Measures…The Fundamental Theorems of…The Law of One PriceThe Pricing KernelDiscount Factors and Zero-Coupon PricesReplication vs HedgingComplete Market vs Incomplete MarketClearing Margin Architecture
viOption Pricing Theory
European and American OptionsMonte Carlo European OptionThe Black-Scholes PDEBlack Scholes and the GreeksThe Payoff FunctionThe Binomial ModelBinomial Option PricingDelta Hedging in TheoryBoundary, Initial and Terminal ConditionsThe Exercise BoundaryHow to Check Put-Call…
viiVolatility Models
Constant, Local and Stochastic…Vasicek Model vs CIR ModelThe Heston ModelThe SABR ModelThe Volatility ProcessImplied VolatilityVolatility Smile vs Skew vs Surface
viiiInterest Rate Models
Interest-Rate DerivativesMean ReversionThe Zero-Coupon BondThe Ornstein-Uhlenbeck ProcessThe Discount CurveZero RatesShort-Rate Model vs Market Model
ixNumerical Pricing
Closed Form and Numerical…Monte Carlo PricingEuler and Milstein Schemes ComparedTree MethodsFinite Difference MethodsNumerical Error and StabilityVariance Reduction
xCalibration and Model Risk
Model OverrideMarket Price and Model PriceCalibrationHow to Document a Pricing ModelThe Educational Illustration LabelMarket ConventionsModel Uncertainty and LimitationsBacktesting a Pricing ModelIdentifiabilityCalibrated ParametersThe Calibration Loss Function

Selection and Survivorship Bias: When the Sample Was Never Representative

A record is unrepresentative when the things inside it were chosen in a way that moves the answer. The Neelbagh market office, invented, keeps only stalls that are still trading, so two that closed were deleted along with every month they traded. Month 1 then averages Rs 38,500/- across the eight kept and Rs 35,000/- across the ten that really traded, 10.00 per cent apart.

Two things are already in hand, and both get used below without being rebuilt. The Neelbagh stall record, invented for these notes, has already been printed and walked through: four months numbered 1 to 4, one row for each stall in each month, and eight columns whose meanings are written down. And the arithmetic averageAdd up the numbers that are usable, then divide by how many were used. Nothing more than that, and it is settled in full separately. has been defined along with the convention this record uses for it. The convention is repeated below every time a figure appears.

The convention decides every average below, so here it is in one line. The average takingsThe money that came across the counter during a month, counted before rent, wages, stock or anything else is paid out of it. A gross figure, not a profit. per stall month is the sum of the takings cells that carry a usable number, divided by how many carry one. A blank is not a number. The office code for a return that never arrived is not a number. A real zero is a number. Where a division below uses some other divisor, the divisor is named in the same sentence as the figure.

Nothing else is assumed. The whole argument is arithmetic on ten whole numbers, and it turns on which ten numbers were handed over and which ten were not. The word bias is used here in one narrow sense only: the record was built out of the wrong set of things. Bias in that narrow sense says nothing about a procedure, a formula or a long run.

What does it mean for a record to be unrepresentative?

A record is a set of things somebody decided to write down. Somebody drew a line around what would be included, somebody kept it up over time, and somebody decided what happened to a row when the thing it described stopped existing. Every one of those is a decision made by a person, before the file is ever opened.

A record is unrepresentative when the way things got into it is connected to the thing being measured. That connection is the whole fault. If entries arrive and leave for reasons that have nothing to do with the quantity of interest, the record can be small, scrappy and still honest. If entries leave precisely because of that quantity, the record can be enormous, tidy and completely wrong.

Here is the everyday version, and it costs nothing to picture. Somebody stands outside a cinema at the end of a film and asks ten people coming out whether the film was any good. Ten answers come back, and they can be averaged. Walking out is exactly what stops somebody being available to ask, so the four people who walked out at the interval are never met. The people missing from those ten are missing for the very reason the question was being asked, and no amount of politeness in the questioning repairs that.

Now notice what does not fix it. Asking a hundred people instead of ten does not fix it. Asking on three different evenings does not fix it. A bigger record gathered the same way is wrong by the same amount, and the only thing that grows is the confidence with which the wrong figure gets quoted. Size and correctness are separate properties, and a fault in how things were chosen is untouched by how many were chosen.

Try it out

Does collecting more rows fix an unrepresentative record?

Breaking Into Quants Bootcamp — Fin Maverick

Who decided what this record would ever cover?

Before a single row exists, somebody settles which things the record will cover. The Neelbagh market has fourteen pitchesA marked out patch of floor inside a covered market, let by the month. It has no walls of its own, and a trader who takes one brings the stall.. In month 1, ten of them were trading and four stood empty. All four stood empty across all four months, so they produce no rows and never could. The market office keeps a record of trading stalls, and an empty pitch is not a trading stall.

The set of ten trading stalls is the frame, and the frame answers one question: what was this record ever going to cover. A frame is a decision rather than a fact. The office could have written the record differently, keeping one row per pitch per month with the empty ones showing nothing, and then fourteen would have been the frame. The office chose not to, and that choice was made before the first row was written and is nowhere recorded inside the file.

FOURTEEN PITCHES. TEN TRADED. THE FILE HOLDS EIGHT. NB-01 Kadamba Idli in the file NB-02 Chandan Tea in the file NB-03 Harit Greens in the file NB-04 Peetal Utensils in the file NB-05 Bansi Flour in the file NB-06 Ilaka Fruit in the file NB-07 Sundari Chaat in the file NB-08 Peeli Mithai in the file NB-09 Roshni Juice deleted NB-10 Amber Rolls deleted no stall never let no stall never let no stall never let no stall never let in the file the office hands over deleted after closing no stall ever took the pitch Invented illustration. The Neelbagh market and every stall in it were made up for these notes.
The Neelbagh market has fourteen pitches, ten of which traded in month 1, and the file the office hands over holds eight, so two different shrinkages have already happened before anybody opens it.

Four pitches standing empty is not a fault in the record. The record was built to cover trading stalls, and an empty pitch was never going to produce a row. The empty pitches are a frame, not a loss. But a frame decides which questions a record can answer, and the moment the frame is forgotten, the record gets used to answer a question it was never built for.

Try it out

Four of the Neelbagh market's fourteen pitches stood empty across all four months. Is that a fault in the record?

Why do three different averages of the same month's money all come out right?

Month 1 is worth dwelling on because it has no faults in it at all. No blank, no repeated row, no odd code, no misspelling. Ten stalls traded, ten rows were written, and the takings add to Rs 3,50,000/-. Everything that follows comes out of that one clean month.

Rs 3,50,000/- divided by the ten stalls that traded gives Rs 35,000/-. The same Rs 3,50,000/- divided by all fourteen pitches in the market gives Rs 25,000/-, lower by 28.57 per cent. Both divisions are correct. The two divisions are not two attempts at one figure, one of them sloppier than the other. Each answers a different question. The first says what a trading stall took in month 1. The second says what a pitch in this market earned in month 1, counting the empty ones as the nothing they earned.

Choosing between those two divisors is selection, and it is worth being exact about what moved. The money did not move. Nobody deleted a row, nobody lost a return, nobody hid anything. The person doing the dividing changed their mind about which set they were describing, so the denominatorThe number that is divided by. It sits underneath the line in a division, and it is what decides which set the answer is an average over. changed. Selection changes the denominator and leaves the money alone, and the only harm it does is when the denominator goes unstated.

ONE MONTH, THREE AVERAGES, THREE DIFFERENT QUESTIONS THE MONEY DIVIDED BY GIVES WHICH ANSWERS Rs 3,50,000/- 14 pitches in the market Rs 25,000/- what a pitch earned Rs 3,50,000/- 10 stalls that traded Rs 35,000/- what a trading stall took Rs 3,08,000/- 8 stalls the office kept Rs 38,500/- what the office file says ROWS ONE AND TWO SHARE ONE NUMERATOR. ROW THREE DOES NOT. The third figure changes the money as well as the count, which is why it is a different fault.
One month's Rs 3,50,000/- gives Rs 25,000/- over fourteen pitches and Rs 35,000/- over ten trading stalls, while the third figure of Rs 38,500/- reaches its answer by changing the money too.
Try it out

Month 1's takings across the market are Rs 3,50,000/-. Which is the right average: Rs 25,000/- or Rs 35,000/-?

AI For Finance Bootcamp — Fin Maverick

What is survivorship bias, and why is it not the same fault as selection?

Now the second fault, and it arrives quietly. The market office does not keep a historical archive. For rent, for licences and for the notice board, the office needs only the stalls currently trading, and a working file of those is what it keeps. NB-09 Roshni Juice and NB-10 Amber Rolls both closed after month 2. When they closed, the office removed them from the file, and it removed them completely: their month 1 rows and their month 2 rows went with them.

Look at what that does to month 1 inside the file. The takings no longer add to Rs 3,50,000/-. Rs 20,000/- and Rs 22,000/- have left, so the takings inside the file add to Rs 3,08,000/-. The count is no longer ten. It is eight. Survivorship changes the money and the count together. Selection moves only the count, and that is precisely why the two faults must never be given one name.

Feel the difference with something ordinary. A shopkeeper who counts every till in a street, including the two that took nothing, and one who counts only the tills that rang, are doing selection: same street, same money, different denominator, and either answer is fine as long as they say which they meant. A shopkeeper who quietly throws away the takings slips of every shop that shut during the year is doing something else entirely. Both the total and the count have shifted, and the slips are gone, so nobody looking at the drawer afterwards can even tell.

SELECTION the denominator moves, the money does not SURVIVORSHIP the money moves too, and the file is silent UNCHANGED Rs 3,50,000/- 14 pitches 10 stalls gives Rs 25,000/- Rs 35,000/- One move. Nobody deleted anything, and both answers are correct once the divisor is named. CHANGED Rs 3,50,000/- Rs 3,08,000/- 10 stalls 8 stalls gives Rs 35,000/- Rs 38,500/- Two moves at once, and no line anywhere in the file records that either of them happened.
Selection changes the denominator and leaves the money alone, while survivorship changes both at the same time, which is the reason the two faults are never given one name.

The worked instance: month 1, printed whole

Here is every stall that traded in month 1 of the Neelbagh stall record, with what it took and whether it is in the file the office hands over.

StallNameTook in month 1In the office file?
NB-01Kadamba IdliRs 42,000/-yes
NB-02Chandan TeaRs 31,000/-yes
NB-03Harit GreensRs 38,000/-yes
NB-04Peetal UtensilsRs 26,000/-yes
NB-05Bansi FlourRs 55,000/-yes
NB-06Ilaka FruitRs 34,000/-yes
NB-07Sundari ChaatRs 40,000/-yes
NB-08Peeli MithaiRs 42,000/-yes
NB-09Roshni JuiceRs 20,000/-no, closed after month 2 and deleted
NB-10Amber RollsRs 22,000/-no, closed after month 2 and deleted
Ten stalls that tradedRs 3,50,000/-eight of them survive in the file

And the three averages, each printed with the divisor that made it, under the convention named at the top.

Money dividedDivided byAverage takings per stall month
Rs 3,50,000/-14 pitches in the marketRs 25,000/-
Rs 3,50,000/-10 stalls that tradedRs 35,000/-
Rs 3,08,000/-8 stalls the office keptRs 38,500/-

Read the left column of that second table before anything else. The first two rows divide the same money. The third does not, and the Rs 42,000/- difference in the numerator is the two deleted stalls. Printing the three figures side by side without saying that the third one moved the money too draws a picture in which survivorship looks like one more choice of divisor, and it is not.

Risk Management Program Bootcamp — Fin Maverick

What does survivorship cost here, in rupees?

Month 1 averages Rs 38,500/- across the eight the office kept and Rs 35,000/- across the ten that actually traded. The gap is Rs 3,500/- per stall monthOne stall for one month, which is the thing being counted here. Ten stalls across four months would make forty of them at most., and that gap is exactly 10.00 per cent above the honest figure. The fault is worth Rs 3,500/- a stall a month, stated as money rather than as a caution.

Ask why it is that large and the answer is the mechanism itself. The two stalls that closed were the two smallest takers in month 1, at Rs 20,000/- and Rs 22,000/-. The two averaged Rs 21,000/-, a full 40.00 per cent below what the ten averaged. The office deleted 20.00 per cent of the market, and it deleted the weakest fifth of it. A record never loses a random selection of what was in it. Here the reason a stall left is the very quantity being measured, its takings.

The whole of survivorship sits in that mechanism, and the mechanism is worth stating twice. Closing is not an accident that strikes stalls at random. A stall closes because it was not taking enough. Takings is also the column being averaged. So the reason for removal and the quantity being measured are the same quantity, and every removal pushes the average one way only.

Try it out

When month 1's weakest stalls are dropped from the record one at a time, does the average move a lot or a little?

Play with it

Drop month 1's weakest stalls one at a time, then look at the same record the way its file would show it.

One control moves: how many of month 1's ten stalls have been dropped, always from the weakest upward, from none to four. The ten takings figures never move; the record wrote them once and they stay written. As the control is dragged, the dropped bars grey out, the average line slides upward and the live sentence restates the reading. The second button below redraws the same setting as a complete file, with the dropped stalls gone rather than greyed, and a file handed over looks exactly like that. The panel opens at two dropped, the number the market office deleted.

Jump to a setting used above:

Stalls dropped from the weakest upward: 2
MONTH 1, TEN STALLS IN ORDER. THE WEAKEST LEAVE FIRST. 0 14,000 28,000 42,000 56,000 takings, Rs Invented illustration. Ten made up figures from month 1 of the Neelbagh stall record. No real market is described.
With two stalls dropped from the weakest upward, eight remain and their takings of Rs 3,08,000/- average Rs 38,500/- per stall month, which sits Rs 3,500/- above the Rs 35,000/- the ten that traded actually averaged.
Stalls left
8
Their average
Rs 38,500/-
Above the honest figure
Rs 3,500/-
Faults a check would find
none, though 2 left
At the setting the panel opens on, two dropped: eight stalls remain, they average Rs 38,500/- per stall month, and that sits Rs 3,500/- above the Rs 35,000/- averaged by the ten that traded, or 10.00 per cent above it. The second button redraws the same eight as a finished file, on which nothing looks short of anything.
Educational illustration. The Neelbagh market, the Neelbagh stall record and all ten takings figures were invented for these notes. The ten figures are held fixed and only the number dropped moves; stalls are always dropped from the weakest upward, the order in which closing takes them; and every amount is carried as a whole number of paise so that no reading drifts as the control is moved.
THE TWO THAT CLOSED SIT AT THE SHORT END, AND THAT IS THE FAULT 0 14,000 28,000 42,000 56,000 takings, Rs Rs 20,000/- NB-09 Rs 22,000/- NB-10 Rs 26,000/- NB-04 Rs 31,000/- NB-02 Rs 34,000/- NB-06 Rs 38,000/- NB-03 Rs 40,000/- NB-07 Rs 42,000/- NB-01 Rs 42,000/- NB-08 Rs 55,000/- NB-05 Rs 38,500/- over 8 Rs 35,000/- over 10 these two closed after month 2 and were deleted Invented illustration. Every figure is made up and no real market is described.
The two stalls that closed were the two smallest takers in month 1, at Rs 20,000/- and Rs 22,000/-, and removing them lifts the average from Rs 35,000/- to Rs 38,500/-, a rise of Rs 3,500/- a stall a month.
Try it out

The eight surviving stalls averaged Rs 38,500/- in month 1 and all ten averaged Rs 35,000/-. What is the overstatement?

Why does a file with this fault in it look completely clean?

Take the six counts that get run against any record of this shape and run them against the file the office hands over. Rows in the file, thirty two. Distinct pairs of stall and month, thirty one, one short of the rows because one stall month was filed twice. Takings cells carrying a number, thirty one, one short of the rows because one cell is blank. Distinct stall identifiers, eight. Distinct stall names, nine, one more than the identifiers because one stall is spelled two ways. Stall months the eight by four grid expects, thirty two, one more than the pairs present because one row never arrived.

Four faults show up as a gap of one between two counts. Now put the four deleted rows back, run all six counts again, and watch what happens. Every count rises. Not one gap changes size. A check reads what is there, and the deleted rows are not there to be read, so the fault that costs most in this record leaves no trace in it at all.

SIX COUNTS, RUN TWICE. EVERY COUNT MOVES. NO GAP MOVES. THE COUNT THE FILE AS HANDED OVER WITH THE FOUR ROWS PUT BACK WHAT THE GAP SHOWS rows in the file 32 36 the base for the two below distinct pairs of stall and month 31 35 gap of 1: two rows, one key takings cells carrying a number 31 35 gap of 1: the blank cell distinct stall identifiers 8 10 the base for the row below distinct stall names 9 11 gap of 1: two spellings stall months the grid expects 32 36 gap of 1: the absent row THE TWO DELETED STALLS: A GAP OF NOTHING, IN EITHER COLUMN Restoring the four rows lifts every count on this table and leaves all four gaps at exactly one. A check shows what is wrong inside a file. It cannot show what is missing from it.
Every count on the file comes out exactly as a clean eight stall file would, so the fault that costs the most rupees is the one fault that leaves no trace in the record at all.

One more column shows how thorough a clean profile can look while still saying nothing. The filed on dayA column in this record holding the day of the following month on which a stall's monthly return reached the office. It is used here only as one more column that checks out. column is complete on every row in the file, in range on every row, and perfectly consistent. The two deleted stalls filed on time in both months they traded, so the column was complete for them too. With those rows gone, nobody will ever know it.

Try it out

Every count on the office's file comes out clean. What does that establish about survivorship in it?

Investment Banking Analyst Bootcamp — Fin Maverick

Is one stall month missing from this file, or nine?

Two correct answers make this question worth asking. Lay the file against the grid it implies: eight stalls, four months, thirty two stall months expected. Thirty one distinct pairs are present, so one is missing. The missing pair is NB-04 Peetal Utensils in month 3, whose return never arrived.

Now lay the same file against the grid the market really had: ten stalls, four months, forty stall months. Thirty one are present, so nine are missing. One of those nine is the absent return. Four are the rows the office deleted, being NB-09 and NB-10 in months 1 and 2. The remaining four are months 3 and 4 for those two stalls, when they no longer existed at all.

The grid was chosen before the file was written, and nothing inside the file records the choice, so both counts are correct arithmetic and the file cannot show which grid is right. A class register with the students who left already rubbed out adds up perfectly, every time it is checked, forever.

THE GRID THE FILE IMPLIES: 8 BY 4 THE GRID THE MARKET REALLY HAD: 10 BY 4 1 2 3 4 1 2 3 4 NB-01 NB-02 NB-03 NB-04 NB-05 NB-06 NB-07 NB-08 ONE HOLE NB-01 NB-02 NB-03 NB-04 NB-05 NB-06 NB-07 NB-08 NB-09 NB-10 NINE HOLES a row that is in the file no row, though the stall was trading a row the office deleted months these two stalls no longer existed
Checked against the grid the file implies, one stall month is missing; checked against the grid the market really had, nine are, and nothing inside the file settles which grid was meant.
Try it out

Is one stall month missing from this file, or nine?

Bond Pricing and Yield Mechanics — free micro-course from Fin Maverick

What should be asked of any record before it is averaged?

Everything above is one market and ten numbers, but the habit it teaches is portable, and it is four questions long. A lender sizing a loan against a trade whose failures quietly leave the books, an analyst reading a set of results supplied by whoever is still around to supply them, and a household comparing what neighbours say they spend on school fees are all standing in the same place. The four questions are put to any record, in this order, before anything is divided by anything.

FOUR QUESTIONS, AND ONLY THE LAST ONE SPLITS 1. What was this record built to cover, and what fell outside from the start? 2. Does anything ever get removed from it, and who decides that? 3. When something is removed, does its history go with it? 4. Is the reason for removal connected to what is being measured? YES This is survivorship. The figure moves, and nothing inside the file will ever say so. NO Not survivorship. Go back to question one and state which set the divisor counted. Every one of these is answered by asking the person who keeps the record, not by reading the record.
What the record was built to cover, whether anything is removed from it, whether its history goes too, and whether the reason for removal is connected to what is being measured.

Question four is the one that does the work. A yes to it is the whole of survivorship, and here is the uncomfortable part: the answers are facts about how the file is kept rather than facts inside it, so not one of the four questions can be answered by reading the file. The answers come from asking the keeper. In the Neelbagh market the answer to question two sits in the office day bookA paper notebook the office writes sentences into, one line for each thing that happened, with no columns at all. What a notebook like that can and cannot answer is covered separately., in a line about two stalls handing back their pitches. A notebook of sentences has its own treatment. The narrow claim here is that the sentence exists, it is not in the file, and nobody who only opened the file will ever go looking for it.

What went wrong: a leaflet whose arithmetic was perfect

The Neelbagh market office prints a short leaflet for traders asking about a vacant pitch. The leaflet quotes Rs 38,500/- a month as what a stall in this market takes. The figure is computed correctly, from the file the office keeps, under the convention repeated throughout. Nobody lied and nobody miscalculated.

Every stall in that file is still trading, and being still trading is exactly why it is in the file. The two stalls that took Rs 20,000/- and Rs 22,000/- and then shut are not in it. Neither are the four pitches nobody ever took. So a trader reading the leaflet is shown Rs 38,500/- for a market whose trading stalls averaged Rs 35,000/- and whose pitches averaged Rs 25,000/-, and the two figures they most needed are the two the file cannot produce.

The habit that fixes it is to ask what left the record before averaging anything, and to take the answer from the person who keeps it rather than from the record itself. Had the leaflet carried one extra sentence naming what the figure was divided by and what had been removed, the arithmetic would have been unchanged and the reader would have been in a completely different position.

Try it out

The office leaflet quotes Rs 38,500/- a month, computed correctly. What is the trader not being told?

Where each of these threads is picked up. The counts run against a file, and what each of them can and cannot expose, are covered separately. Missing values inside a record, why a cell is empty and what that decides about the fix, are covered separately, as are rows that repeat and what they distort. Whether a large figure is a real month or a recording error is a question about provenance rather than about size, and it has its own treatment. How research gets designed so that a result is able to fail honestly, and so that anybody can retrace which data produced which answer, is covered separately again and sits outside these notes on data.

Hypothesis Testing teaches you to run a test, say what it can and cannot support, and recognise a manufactured result.

What was read to produce these figures?

Nothing was read. Every figure below was invented for teaching and then checked by working the arithmetic twice, once forward from the ten takings figures and once backward from each average.

The material usedWhat it gaveStanding
The Neelbagh stall record, month 1Ten takings figures totalling Rs 3,50,000/-Invented for teaching, and faulted on purpose
The market office fileThe eight stalls kept and the two deletedInvented for teaching
The Neelbagh pitch listFourteen pitches, ten of them tradingInvented for teaching
The arithmetic, worked twiceEvery average, gap and share printed aboveRecomputed here, never transcribed

The Neelbagh market, the Neelbagh stall record, the market office and the ten stalls from NB-01 Kadamba Idli to NB-10 Amber Rolls are invented.
Educational material. Not advice on any investment, tax, budget or market position.

← PreviousNext →
Fin Maverick Micro CoursesExplore Micro Courses
Fin Maverick BootcampsExplore Bootcamps
Fin Maverick

Finance education that ends in a job, not a certificate that gathers dust. Built for young India.

LEARN
CalculatorsFrameworksComparisonsCareersShowdown
RESOURCES
All CoursesMicro CoursesBootcampsInternships
COMPANY
AboutJob openingPartnership
LEGAL
Privacy PolicyTerms & ConditionsContent LicenseReturn & Refund Policy
© 2026 FIN MAVERICK / BUILT FOR INDIA.DO FINANCE, DO NOT JUST READ ABOUT IT.