Results for “poisson-sampling”

Compute TAM/SAM/SOM with top-down and bottoms-up methods, plan survey sample sizes with per-segment minimums, and score candidate segments against Kotler's five criteria.

Automates GoPhish phishing simulation campaigns using the Python gophish library to create email templates, configure SMTP profiles, import targets, launch campaigns, and analyze results for security awareness assessment.

Process and analyze mass spectrometry data: import spectra from MGF, mzML, MSP, and JSON formats; apply 40+ filters for metadata harmonization and peak cleaning; compute spectral similarities (cosine, modified cosine) for compound identification; build reproducible processing pipelines.

Extracts second-hand item search results from Goofish (闲鱼), China's largest second-hand marketplace. Input a keyword and optional sort/filter parameters to receive a list of item cards with ID, title, price, image, location, and want-count.

Parses betting odds HTML tables with Java and Jsoup, extracting company names, odds values, and change timestamps using specific CSS selectors.

Design and evaluate paid-ad experiments with hypotheses, randomization, sample-size calculations, guardrails, and decision rules for A/B and split tests.

Design experiments and studies before data collection — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable.

Builds a configurable scraping agent that collects data from APIs, HTML, or RSS, enriches it with Gemini AI scoring, and stores results in Notion, Google Sheets, Supabase, or local files.

Guides analysis of Neuropixels recordings from raw data to curated units, covering preprocessing, motion correction, spike sorting, quality metrics, and export.