The problem that no one solved
SPSS costs hundreds of dollars per year. R has a steep learning curve and returns results that require statistical expertise to interpret. Generic online calculators have no declared methodology — you cannot cite them in a journal. And the specific instrument your research needs does not exist anywhere.
This is not a software problem. It is a methodological gap. Researchers in Latin America work with real corpora, heterogeneous data, and complex hypotheses — and they lack instruments that can respond to that specificity with the rigor that international journals demand.
I build that instrument. For your specific problem. With methodology you can cite.
The process
You describe the research problem: the corpus, the variables, the hypothesis, the constraints. I evaluate whether the problem has statistical structure that can be modeled. The initial diagnosis is free and determines whether I can build what you need.
If the case is operable: I select the methodology, I build the instrument in Python or JavaScript, I document the declared methodology — Bootstrap, Monte Carlo, sensitivity analysis — and I deliver a tool with quantified uncertainty that you can use and cite.
You do not need to know statistics. You need to know your research problem.
Where the gap exists
A researcher measuring soil contamination in a mining corridor needs a Bootstrap model that estimates confidence intervals over a corpus of 30 samples. That instrument does not exist. I build it.
An epidemiologist studying disease incidence in a rural district needs sensitivity analysis that identifies which variables determine risk and which are noise. That instrument does not exist. I build it.
A sociologist analyzing survey responses needs Bonferroni correction when testing eight simultaneous hypotheses, with documentation that justifies the correction in a journal. That instrument does not exist. I build it.
An agronomist calibrating yield models for a crop under variable climate conditions needs Latin Hypercube Sampling to cover a multidimensional parameter space efficiently. That instrument does not exist. I build it.
An economist modeling price behavior in a regional market with fewer than 50 observable data points needs nonparametric Bootstrap to produce defensible confidence intervals. That instrument does not exist. I build it.
An engineer validating a structural model under uncertain load conditions needs 10,000 Monte Carlo iterations that quantify failure probability before committing a design. That instrument does not exist. I build it.
Declared methodology
Every instrument I build has an explicit, citable methodological declaration. No black boxes. No unexplained results. The method is part of the deliverable.
10,000+ iterations over simultaneous parameter scenarios. Quantifies uncertainty before issuing a criterion. Used in the SAG Calculator validated over 132 MTB suspension models.
Confidence interval estimation without assuming normal distribution. Correct for heterogeneous cultural or biological samples. No arbitrary distributional assumptions.
Identifies which variables determine the result and which are noise. Prioritizes where to invest measurement effort. Citable with specific DOI literature.
Controls Type I error when testing multiple hypotheses simultaneously. Justified and documented — reviewers cannot reject it for lack of methodological support.
Efficient coverage of multidimensional parameter spaces. Reduces the number of simulations needed without losing representativeness. Ideal for complex models with correlated variables.
Kolmogorov-Smirnov and Shapiro-Wilk to verify that model distributions correspond to real data. Not assumed — demonstrated.
Related reading
The methodology behind these instruments has published backing. These articles document the theoretical and empirical framework:
Frequent questions
The instrument is methodological, not disciplinary. Environmental sciences, epidemiology, social sciences, econometrics, cultural anthropology, engineering, agronomy. What matters is that the problem has statistical structure to model. The initial diagnosis determines whether it is operable.
It is different. SPSS and Stata are generic platforms. What I build are specific instruments for your research problem, with declared methodology and Latin American context. They do not replace general statistical software — they solve what that software cannot solve well for your specific corpus.
It depends on the complexity of the corpus and the required methodology. The initial diagnosis — free — determines the scope and the actual time. No optimistic estimates: the diagnosis measures it.
Only what is necessary to calibrate the model. Everything under a confidentiality agreement. It is not necessary to share the complete corpus — only the calibration parameters.
Describe your research problem.
If the case is operable, I tell you what instrument can be built, what methodology applies, and what the timeline looks like. No commitment. No generic quotes.
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