In hypothesis testing, two types of errors can occur when making a decision about the null hypothesis.
These errors are known as Type I and Type II errors.
Additionally, the Standard Error of Mean (SEM) is an important concept that measures the precision of the sample mean estimate.
Definition
A Type I error occurs when the null hypothesis (H0) is true, but it is incorrectly rejected.
It is also known as a "false positive" or "alpha error."
Characteristics
Significance Level (α): The probability of committing a Type I error is denoted by the significance level (ααα). Common values for ααα are 0.05, 0.01, and 0.10.
Example: If α=0.05, there is a 5% chance of rejecting the null hypothesis when it is actually true.
Consequences
Type I errors can lead to the conclusion that an effect or difference exists when it does not, potentially leading to incorrect scientific conclusions and implications.
Example in Pharmaceuticals
Suppose a pharmaceutical company tests a new drug and concludes it is effective (rejecting H0) when, in reality, it is not.
This can lead to the false belief that the drug works, resulting in wasted resources and potential harm to patients.