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Assessing risks from genomic data is not like using a pregnancy-test kit with its bright blue line.
However, whatever the imputation method chosen, imputed data is not like observed data because it has inherent uncertainty.
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In contrast, geopolitical boundary limits on the data were not liked.
But forecasting economic data isn't like setting a price target on a stock: a slightly less-dire prediction doesn't make anyone happy, and analysts' forecasts don't, obviously, have any impact on the actual data (which is very different from the stock market).
People using big data are not like novelists, ministers, psychologists, memoirists or gossips, coming up with intuitive narratives to explain the causal chains of why things are happening.
Interpreting this all very much depends on how you define public service spending and in either case the data is not comparable on a like-for-like basis to the 1930s (for example the NHS was founded in 1948) without a degree of additional explanation not evident in the immediacy of this poster.
Unfortunately, Facebook's "like" data is not exactly the same thing as a list of recommendations, especially when it comes to the businesses that have established a presence on the site.
Its unique selling point is that its ad data is not based around cookies like many services, but on registered, named users.
Their functional data is not strong enough for other iPSC-like lines.
Selling anonymized data is not uncommon for free services like Unroll.me and its owner Slice — Slice even pitches its powerful data set publicly.
The relation between the unknown parameters and the observations, i.e. the satellite trajectory in case of hl-SST data and the range in the case of ll-SST data, is not linear, requiring linearisation (much like the variational equations approach).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com