March 15, 2018 (Vol. 38, No. 6)
Strong Points: Interesting background material, many free tools
Weak Points: Narrow focus
As it turns out, there are many uses for randomness in scientific analyses (as well as everyday applications). For example, scientists often use random number generators to create mock data distributions for statistical analyses, and randomly generated datasets are also integral to many modeling applications. Yet, many of the algorithms that generate “random” numbers are only pseudorandom, meaning that there is some intrinsic mathematical logic that leads to the production of numbers that only appear random. The random number generators on Random.org, however, are true random number generators, and can be used to generate random sequences of integers, Gaussian distributions, and DNA and protein sequences, among other things. These tools are free to use (though there are some paid services available on the site, too). In addition to the random number generators, there is also information about randomness in general, including why random numbers are interesting and difficult to generate.