A Type II error is failing to reject the null hypothesis when in fact it is false. This is true
Null Hypothesis (H₀): A statement that assumes no effect or relationship exists between variables.
Type II Error: Occurs when you fail to reject the null hypothesis, even though it's actually false in reality. It's essentially a "false negative" result.
Type II errors often result from insufficient sample sizes or low statistical power. They can have significant consequences in various fields, such as medical research, clinical trials, and quality control. It's crucial to consider both Type I and Type II errors when designing studies and interpreting results.
A Type II error is failing to reject the null hypothesis when in fact it is false. true or false