Easy question. Sampling techniques.

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The discussion focuses on identifying the sampling technique used to select nursing supervisors for salary determination. Participants are asked to choose from several options, with emphasis on the use of random numbers. The consensus suggests that the correct answer is "a. Random," as random numbers indicate a random sampling method. There is a request for clarification on the reasoning behind the answer, highlighting confusion about the sampling techniques. The conversation underscores the importance of understanding sampling methods in data collection.
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Identify the sampling technique used to collect the data.
Nursing supervisors are selected using random numbers in order to determine annual salaries.
Choose one answer.
a. Random
b. Stratified
c. Systematic
d. Cluster
e. Convenience

This one is DESTORYING me..

EDIT: Please explain why you got your answer.
 
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Unless this is a trick question, all I know is that random numbers are used for random sampling.
 

Thread 'Hypothesis testing: Defining H0, HA hypotheses so that ( H_A)_A' makes sense'

The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...
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