TYPE I ERROR Meaning and
Definition
-
A type I error, also known as a false positive, is a statistical mistake that occurs when a null hypothesis is rejected but it is actually true. In hypothesis testing, a null hypothesis assumes that there is no significant difference or relationship between variables being studied. A type I error happens when researchers, due to various reasons, mistakenly reject the null hypothesis and conclude that there is a significant effect or relationship when there isn't one in reality.
The probability of committing a type I error is denoted by the Greek letter alpha (α). It represents the significance level or the chosen threshold below which the null hypothesis is rejected. A lower significance level means that stronger evidence is required to reject the null hypothesis, reducing the chances of a type I error. Commonly used significance levels are 0.05 (5%) and 0.01 (1%).
Type I errors can occur due to various factors such as sampling error, faulty experimental design, or an inappropriate statistical model. It is important to minimize the occurrence of type I errors in scientific research as they can lead to false conclusions and waste resources on further investigation or implementation of unnecessary actions based on incorrect findings.
To mitigate the risk of type I errors, researchers can design experiments with larger sample sizes or conduct multiple tests to ensure consistency and reliability in their findings. Additionally, peer review and replication of studies by other researchers provide checks and balances to identify and rectify potential type I errors in research.
Common Misspellings for TYPE I ERROR
- rype i error
- fype i error
- gype i error
- yype i error
- 6ype i error
- 5ype i error
- ttpe i error
- tgpe i error
- thpe i error
- tupe i error
- t7pe i error
- t6pe i error
- tyoe i error
- tyle i error
- ty0e i error
- typw i error
- typs i error
- typd i error
- typr i error
- typ4 i error
Infographic
Add the infographic to your website:
