Sentence examples for meaningless information from inspiring English sources

Meanwhile, viewers at home were bombarded with simultaneous streams of equally meaningless information, from picture-in-picture footage from two sections of the red carpet to a crawl at the bottom of the screen spitting out fun facts.

In place of distinct emotions, it experiences a blur of sensations, a condition Derbyshire has likened to looking at "a vast TV screen with all of the world's information upon it from a distance of one inch; a great buzzing mass of meaningless information," he writes.

Levan claims that Bove's report that alleged his bank has capital levels in the "danger zone" didn't look at the right data: "Instead, he only examined holding company data which, in at least our case, is meaningless information".

Hofstra's examples of "subtle hazing" include but aren't limited to deception, demerits, quizzing on meaningless information, name calling and requiring new members to refer to current members by titles such as Mr. or Miss.

This is contrasted with the 1 bit/word needed to determine the outcome of a Bernoulli trial, and the average 2.161±0.095 bits/word (95% confidence interval) of predominantly meaningless information that would have been added if the editor had chosen the rarer word in all 756 cases.

CoIN also allows biocurators to survey the ranking lists for specific queries without reviewing meaningless information.

His choices average 0.26±0.20 bits higher in entropy information (95% confidence interval, P = 0.005), as against the single bit that determines the outcome of a coin toss, and the average 2.16±0.10 bits (95%) of (predominantly meaningless) entropy information if the rarer word had always been chosen.

However, Internet users are faced with a lot of garbage information and meaningless data [2].

Seng and Chen (2010) suggested that the basic challenge is how to convert seemingly meaningless data into useful information and competitive intelligence.

To solve a large number of Internet garbage information and meaningless data reduce the user's Internet experience, according to the changing needs of users, the cooperative embedded filter crowd content recommendation mechanism was proposed to enhance the efficiency and accuracy of use information from the large amount of network resources.

Only when Hispanics are defined as African-American or the data are loaded with meaningless extra information do effects attenuate.