miss, false negative, type II error
correct stimuli, no response
Miss is a term used in statistics to refer to a scenario where a statistical model fails to detect important information or patterns in a dataset. In other words, it is the occurrence of a negative outcome when a positive one was expected. For instance, a medical test that fails to identify a disease when it is present is considered a miss.
False negative is a statistical term that refers to a situation where a test result comes out negative, meaning that there is no condition present, when in fact, there is a condition present. This type of error can have serious consequences in fields such as medicine or criminal justice, where the failure to identify a disease or a criminal can result in harm to individuals or society.
Type II error is a statistical concept that occurs when a null hypothesis is not rejected when it should be rejected. In other words, it is a situation where a sample is tested, and the test concludes that there is no statistical difference between the sample and the population, even though there actually is a difference. This type of error is also known as a false negative. Type II errors are often caused by small sample sizes or low statistical power, and they can lead to incorrect conclusions and poor decision-making.
More Answers:
Understanding Mood-Congruent Memory: The Impact of Emotional States on Memory Retrieval.Mastering Monocular Cues: How One Eye Can Provide Depth and Distance Perception
Understanding the Modal Model of Memory: Sensory, Short-Term, and Long-Term Memory Processing and Storage Explained.