Question

when a hypothesis predicts that there is no correlation in the population, this is known as a
negative hypothesis
alternative hypothesis
null hypothesis
not sure hypothesis
a pearson correlation is calculated for a sample of 50 individuals. what value of df (degree of freedom) should be used to test the significance of the correlation?
49
48
48
50
as a rule of thumb, researchers should almost expect some error (i.e., sampling error) between a sample correlation and the population correlation it represents.
true
false

Explanation:

Question 1:

• Negative hypothesis: Not a standard term in hypothesis testing for correlation.
• Alternative hypothesis: Claims there is a correlation (opposite of "no correlation").
• Null hypothesis: States there is no relationship (e.g., no correlation in the population).
• Non - zero hypothesis: Not a standard term for "no correlation" claim.

Question 2:

Step1: Formula for degrees of freedom in correlation

The formula for degrees of freedom ($df$) when testing the significance of a Pearson correlation is $df=n - 2$, where $n$ is the sample size.

Step2: Substitute $n = 50$ into the formula

Given $n = 50$, then $df=50 - 2=48$.

Question 3:

In statistics, due to sampling variability (random selection of samples from a population), there is almost always some sampling error. A sample is just an estimate of the population parameter (like correlation). So, researchers should almost expect some error between a sample correlation and the population correlation it represents.

Answer:

1. C. null hypothesis
2. 48
3. True