#Sample Size

Sample size is the magical number statisticians and marketers flaunt when droning on about "reliability." Larger is purportedly closer to the truth, yet at any stage it conveniently doubles as an excuse factory claiming "if only it were bigger." The value of experimental results hinges entirely on this figure, and once deemed "adequate," nobody bothers inspecting the data itself. Complaints of "insufficient sample size" are not signs of a flawed methodology but rather time-tested ploys to bury inconvenient findings.