A number of recent studies have investigated online anony- mous (“dark web”) marketplaces. Almost all leverage a “measurement-by-proxy” design, in which researchers scrape market public pages, and take buyer reviews as a proxy for ac- tual transactions, to gain insights into market size and revenue. Yet, we do not know if and how this method biases results. We build a framework to reason about marketplace mea- surement accuracy, and use it to contrast estimates projected from scrapes of Hansa Market with data from a back-end database seized by the police. We further investigate, by sim- ulation, the impact of scraping frequency, consistency and rate-limits. We find that, even with a decent scraping regimen, one might miss approximately 46% of objects – with scraped listings differing significantly from not-scraped listings on price, views and product categories. This bias also impacts revenue calculations. We find Hansa’s total market revenue to be US $50M, which projections based on our scrapes un- derestimate by a factor of four. Simulations further show that studies based on one or two scrapes are likely to suffer from a very poor coverage (on average, 14% to 30%, respectively). A high scraping frequency is crucial to achieve reliable coverage, even without a consistent scraping routine. When high-frequency scraping is difficult, e.g., due to deployed anti- scraping countermeasures, innovative scraper design, such as scraping most popular listings first, helps improve cover- age. Finally, abundance estimators can provide insights on population coverage when population sizes are unknown.@en