| --- |
| title: LiftLab | Experiment Intelligence Studio |
| emoji: 🧪 |
| colorFrom: indigo |
| colorTo: blue |
| sdk: static |
| app_file: index.html |
| pinned: false |
| license: mit |
| short_description: A/B testing and experiment analytics. |
| tags: |
| - ab-testing |
| - experimentation |
| - conversion-rate-optimization |
| - statistics |
| - hypothesis-testing |
| - analytics |
| - data-visualization |
| --- |
| |
| # LiftLab | Experiment Intelligence Studio |
|
|
| **Hugdrigo portfolio · Project #93** |
|
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| **Short description:** A/B testing and experiment analytics. |
|
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| LiftLab is a polished, local-first application for exploring two-arm conversion experiments, evaluating sampling uncertainty, designing future sample sizes, and maintaining an editable experiment portfolio. It runs as a free **Hugging Face Static Space** using only HTML, CSS and vanilla JavaScript. No server, GPU, external APIs, JavaScript packages, account login, or paid subscriptions are required. |
|
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| > **Educational prototype:** Every included experiment is synthetic. LiftLab uses transparent classical statistical approximations rather than a trained ML model. A small p-value does not demonstrate causality, measure the probability of the hypothesis, validate a product decision, or account for multiple comparisons, repeated peeking, external validity, business guardrails or data-quality problems. Statistical decision-making requires appropriate experimental design and review. |
|
|
| ## Capabilities |
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| - Executive dashboard summarizing experiment volumes, measured conversion lift, signals and experimental uncertainty. |
| - Eight editable synthetic experiments, with search, status filtering, archiving and restoration. |
| - Group-level conversion rates and **Wilson score confidence intervals**. |
| - Absolute and relative treatment lift; relative lift is explicitly undefined if control conversion is zero. |
| - Pooled two-proportion z-test with **two-sided p-value** and minimum expected-cell-count check. |
| - **Newcombe hybrid score confidence interval** for the difference between independent binomial proportions. |
| - Adjustable significance levels: 0.01, 0.05 and 0.10. |
| - Prospective balanced sample-size planning for 80% or 90% power, configurable baseline conversion and target relative lift. |
| - Hypothetical treatment-conversion scenario slider, leaving the recorded experiment unchanged. |
| - Downloadable CSV experiment summary and validated JSON backup/restore, with spreadsheet-formula mitigation and HTML escaping. |
| - Responsive dark/light interface for desktop, tablet and mobile. |
| - Local-only browser persistence through `localStorage` where permitted by browser settings. |
|
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| ## Run locally |
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| Open `index.html` in a modern browser. Some browser security policies restrict local-file execution or storage; in that case run a local static HTTP server, for example: |
|
|
| ```bash |
| python -m http.server 8000 |
| ``` |
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| Open `http://localhost:8000/` in a browser. |
|
|
| ## Publish on Hugging Face |
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|
| 1. Select **Create new Space** in the Hugging Face interface. |
| 2. Owner: `Hugdrigo`; name: `liftlab-experiment-intelligence`. |
| 3. Choose **Static** SDK, **Public** visibility and a blank starting template. |
| 4. Place `README.md`, `index.html` and `LICENSE` in the Space root, preserving this README's YAML front matter. |
| 5. Open the resulting Space and verify its dashboard, Experiment Lab, mobile layout and import/export workflows. |
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| The application is entirely client-side and does not require an inference endpoint. |
|
|
| ## Methodology |
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| ### Definitions |
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| For control count `x_A` out of `n_A` and treatment count `x_B` out of `n_B`: |
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| - Rates: `p_A = x_A/n_A`, `p_B = x_B/n_B`. |
| - Absolute lift: `p_B - p_A` (displayed in percentage points). |
| - Relative lift: `(p_B - p_A)/p_A`, if `p_A > 0`. |
| - Pooled null rate: `(x_A + x_B) / (n_A + n_B)`. |
| - Pooled standard error for a fixed-horizon, equal-rate null hypothesis: `sqrt(p_pool * (1-p_pool) * (1/n_A + 1/n_B))`. |
| - Two-sided z-test p-value: `2 * (1 - Φ(|z|))`; if the pooled standard error is zero, the demo reports p=1. |
| - Wilson score intervals for each group, with z-score according to the chosen two-sided confidence level. |
| - Newcombe difference interval from the Wilson score endpoints: |
| - Lower: `(p_B-p_A) - sqrt((p_B-L_B)^2 + (U_A-p_A)^2)`. |
| - Upper: `(p_B-p_A) + sqrt((U_B-p_B)^2 + (p_A-L_A)^2)`. |
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| A test is marked as having a *statistical signal* only if its unadjusted two-sided p-value is below the selected alpha **and** all four pooled expected success/failure counts are at least five. Otherwise results are labeled inconclusive or insufficient-counts. |
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| ### Sample-size planning |
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| For baseline `p1`, target `p2 = p1 * (1 + relative_lift)`, significance level `alpha`, power `1-beta`, and balanced groups: |
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| `n_per_group ≈ ceil([z_(1-alpha/2)*sqrt(2*pbar*(1-pbar)) + z_(1-beta)*sqrt(p1*(1-p1)+p2*(1-p2))]^2/(p2-p1)^2)`. |
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| This is a conventional large-sample normal approximation for a *future* experiment, not a post-hoc power claim. The normal CDF uses a polynomial approximation to `erf`, and the inverse quantile is computed with bisection. Calculations are not guaranteed accurate for very rare events, tiny experiments or complex experimental designs. |
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| ## Intended safeguards and limitations |
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| - No real customers or data are loaded or transmitted. User-added data remain in the browser unless exported by the user. |
| - A backup is validated for version, shape, bounds, uniqueness and an active selected experiment before replacing state. |
| - HTML-reserved characters in experiment names and channels are escaped before insertion into generated markup. |
| - CSV text fields are quoted and risky leading characters are prefixed to reduce spreadsheet formula interpretation; review any CSV before importing into a spreadsheet. |
| - Experiments are independent two-arm binary-outcome examples. The app does not model clustering, unequal assignment probabilities, interference, seasonality, delayed conversions, covariate adjustment, sequential designs, Bayesian inference, false-discovery-rate corrections, or guardrail trade-offs. |
| - Not a substitute for appropriate experiment governance, data auditing, informed consent or domain-expert analysis. |
| - LocalStorage is not encrypted or synchronized, and browser site-data clearing removes unsaved state. |
|
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| ## Suggested verification |
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| 1. Open Overview, Experiment Lab, Experiment Library and Methodology. |
| 2. Switch selected experiments and confirm all result panels and plots update. |
| 3. Adjust alpha, target lift, power, baseline conversion and hypothetical conversions. |
| 4. Create a new experiment, modify its counts, archive and restore it. |
| 5. Export CSV and JSON, then import the JSON backup after changing local state. |
| 6. Confirm invalid JSON and invalid visitor/conversion counts are rejected. |
| 7. Verify the application at narrow mobile widths and in dark mode. |
|
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| ## License |
|
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| MIT License. See [LICENSE](LICENSE). |
|
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| **Project ownership:** Hugdrigo · Project #93 · 2026. |
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|
