Why HypoTestX?

The Problem

Statistical hypothesis testing is a core skill in data science, research, and engineering. Yet every existing option forces a painful trade-off:

Option A — scipy (and friends)

scipy is excellent, but to use it you must:

  1. Already know which test to run. Is this a t-test or a Mann-Whitney? Should I use Welch’s correction? Is this paired or independent?

  2. Manually prepare your data. Slice the DataFrame yourself, convert to arrays, handle NaN values, split groups by label.

  3. Interpret raw numbers. scipy.stats.ttest_ind() returns a tuple (statistic, p_value). No effect size, no confidence interval, no interpretation.

For a seasoned statistician this is fine. For everyone else — the analyst, the ML engineer, the researcher who is not a statistician by training — it creates a significant barrier.

Option B — Ask a Chat LLM

Modern LLMs can answer “do males earn more than females in my data?” surprisingly well — in natural language. But the answer:

  • Cannot be embedded in code. It’s a paragraph of text, not a structured object.

  • Is not reproducible. Run the same question again and you may get a different answer.

  • Has no audit trail. You cannot verify which test it ran or with which parameters.

  • Cannot be composed. You can’t call .p_value, .effect_size, or .plot() on a chat reply.

The Gap HypoTestX Fills

Feature

scipy

Ask an LLM

HypoTestX

Natural language input

❌

✅

✅

Structured result object

❌

❌

✅

Effect size + CI included

Manual

❌

✅

Reproducible / embeddable

✅

❌

✅

Works offline

✅

❌

✅ (fallback)

Auto test selection

❌

❌

✅

HypoTestX gives you:

  • A plain-English interface so you don’t need to know which test to pick

  • A structured HypoResult with every number you need, every time

  • Effect sizes and confidence intervals included automatically

  • Reproducible, embeddable results you can version-control and audit

  • A regex fallback that works completely offline — no API key, no network

  • An LLM backend system where you swap in any model with a single keyword

When to Use the Direct API Instead

HypoTestX also exposes all 12 test functions directly. Use these when:

  • You already know exactly which test you need

  • You want the most explicit, readable code possible

  • You’re writing a library or module that others will read

# Very explicit — no routing, no ambiguity
result = hx.ttest_2samp(
    group1, group2,
    alternative="greater",
    equal_var=False,   # Welch's correction
    alpha=0.01,
)

The natural-language interface and the direct API return identical HypoResult objects — the same .p_value, .effect_size, .summary(), and .plot() work on both.