Exam 1

1. Metainformation

   Tag	Value
   file	Inferential_Statistics_vufsw-statistical_errors-1126-en_vufsw-statistical_errors-1126-en
   name	vufsw-statistical errors-1126-en
   section	inferential statistics/nhst/statistical errors
   type	schoice
   solution	TRUE, FALSE, FALSE, FALSE
   Type	conceptual
   Program	NA
   Language	English
   Level	statistical reasoning

   Question

   Decide whether the following statements are true or false.

   I. The probability of making a type I error depends on the level of significance.

   2. We make a type II error if we make the mistake that we do not reject the H0, while H0 is in fact false.
   
   
   1. TRUE: Statements I and II are true.
   2. FALSE: Statement I is true and statement II is false.
   3. FALSE: Statement I is not true and statement II is true.
   4. FALSE: Statements I and II are false.
   
   

   Solution

   They are both true.

   1. Wrongly rejecting the null hypothesis, or stating that there is a statistically significant difference in the data when in fact there is not (false positive), is called type I error or alpha error. The probability of type I error depends on the level of significance assigned by the investigator and the existence or nonexistence of a difference between the two experimental conditions.

   A level of significance of 5%, or 1 in 20, is arbitrary set. 5% chance of making a type I error. If p is probability and p <0.05, there is 5% chance that an observed difference occurred because of chance.

   2. A type II error is failing to reject a false null hypothesis (also known as a “false negative” finding)

   EXTRA: Why not always set a very small alpha value? The consequence of setting a p-value of .01 versus .05 is that there is an increased risk of making a type II or beta error. This is a failure to reject the null hypothesis when it is in fact false. The smaller the p-value, the more likely one is to make a type II error. The power of a test is 1-beta. The probability of making a type II error depends on 4 factors. 1) size of alpha (as discussed) 2) variability within a population (more variability results in greater likelihood of type II error) 3) sample size (more subjects results in less chance of type II error) and 4) the magnitude of difference between the experimental conditions (smaller differences result in higher likelihood of type II error).

   If you would like to know more about type I and type II errors watch this  clip

   Levels of Difficulty
   Easy

   M&T Hypothesis testing: means
   Default value

   M&T Hypothesis testing: proportions
   Default value

   
   
   1. True
   2. False
   3. False
   4. False