Final answer:
The significance level of the correlation coefficient is important because it reflects the likelihood that the observed correlation is due to chance. Testing the significance involves a hypothesis test with a carefully chosen significance level that, if exceeded by the p-value, indicates a statistically significant correlation.
Step-by-step explanation:
A researcher must be concerned with the significance level of the correlation coefficient because it tells the researcher how likely it is that the computed correlation value is due to chance or sampling error. This is crucial because it indicates whether there is potentially a real, statistically significant relationship between the variables being studied, or if the observed correlation could simply be a result of random variation.
When testing the significance of the correlation coefficient, researchers use a hypothesis test. The null hypothesis typically states that there is no correlation between the variables (the population correlation coefficient, p, is zero), while the alternative hypothesis posits that there is a significant correlation (p is not zero). For example, if conducting a test at a significance level of 0.05 and obtaining a p-value of 0.04, the researcher would conclude that there is a significant correlation because the p-value is less than the significance level.
The reliability of the correlation coefficient is not just determined by its strength and direction, but also the sample size, as both factors together give us a clearer image of the relationship's significance.
Complete Question:
A researcher must be concerned with the significance level of the correlation coefficient because
A)It is a measure of the validity of the relationship
B)It tells the researcher how likely it is that the computed correlation value is due to chance or sampling error
C)It determines the feasibility for the study
D)Research reports require that significance exist in order to be published