Alpha and Type I Error Relationship
The level of significance, represented by the Greek letter $\alpha$ (alpha), is a critical threshold in hypothesis testing. It defines the probability of rejecting the null hypothesis when it is, in fact, true.
This specific outcome—rejecting a true null hypothesis—is defined as a Type I error.
Consequently, the value of $\alpha$ directly corresponds to the probability of committing a Type I error. An increase in $\alpha$ means that the researcher is willing to accept a higher risk of incorrectly rejecting the null hypothesis.
For example, increasing $\alpha$ from 0.05 to 0.10 increases the probability of a Type I error from 5% to 10%.
Therefore, an increase in alpha directly leads to an increase in the probability of a Type I error occurring.
Important Questions from Measurement and Analysis of Data
- The following are the methods of nutritional assessment using anthropometry.
(i) Skin fold thickness
(ii) 24 hrs dietary recall
(iii) Mid Upper Arm Circumference
(iv) Blood Pressure
(v) Waist Hip Ratio
code : - Which of the following is not a method of Nutritional Status Assessment by anthropometry ?
- The following assumptions must be fulfilled in the Chi-square test of significance
a. data should be continuous.
b. data has to be in frequencies.
c. all observations should be independent.
d. obtained frequencies are equal in all cells.
e. data has to be in ratio scale.
Codes : - Assertion (A) : The hypothesis that the researcher puts to test is called null hypothesis.
Reason (R) : The alternate hypothesis is accepted only when null hypothesis is rejected.
Codes : - Arrange in order the steps in testing for statistical significance :
a. Interpret the results.
b. Select and compute the test for statistical significance.
c. Select a probability of error level.
d. State the research hypothesis.
e. State the null hypothesis.
Codes :
