『有意差なし』という結果になったのは、サンプルサイズが十分でなかったことが原因かも…

This video explains sample size in an easy-to-understand way. Statistical hypothesis testing decisions are never 100% accurate, and there's always the possibility that the decision may differ from the facts. One possible cause of incorrect decisions is an inappropriate sample size. When performing statistical hypothesis testing, a sample size that is too large or too small increases the likelihood of an incorrect decision, so it's important to consider the optimal sample size. This video explains the concept of sample size using the simplest example: a one-sided test of the mean when the population variance is known. Even in cases different from the example discussed this time, such as when the population variance is unknown or a two-sided test, the concept of sample size remains the same. Therefore, to master sample size calculations, it's important to first thoroughly understand the contents of this video. This video can also be useful for studying for your QC certification. We'll continue to provide helpful videos, so we appreciate your support (^-^) = ... Why do we say "we can't say it's not true"? What is a p-value?    • 統計的仮説検定。なぜ「ではないとは言えない」と表現するのか？p値とは？   ▼Watch this before learning the t-test! Testing the mean when the population variance is known.    • t検定を学ぶ前に見るべし！母分散既知の場合の母平均の検定。   ▼The essence of statistical power. Are the test results trustworthy?    • 検出力の本質。その検定結果は果たして信頼に足るのか？   ▼The mean and variance of the distribution that the sample mean follows are surprisingly easy to derive! [Proof]    • 標本平均が従う分布の平均と分散、意外と簡単に導出できちゃいます！［証明］   ==================================================================== Subscribe to this channel ☞   / @datasciencelab.   #QC Certification Level 1 Outstanding Achievement Award Winner Explains