The boundary value that separates significant from non-significant outcomes is called what?

The boundary value that separates significant from non-significant outcomes is called what?

• A. p-value
• B. Standard error
• C. Critical value
• D. Degrees of freedom

Answer: (C)

Critical value is the boundary that separates significant from non-significant outcomes. In hypothesis testing you set a level of risk, called alpha (often 0.05), and compute a test statistic from your data. The critical value is the cutoff on that statistic derived from the chosen distribution. If the test statistic falls beyond this cutoff—outside the central region for a two-tailed test or in the tail for a one-tailed test—you reject the null hypothesis and declare significance. The exact cutoff depends on the test type (z, t, chi-square) and, for t tests, on degrees of freedom. The p-value provides another way to gauge significance, but the critical value is the fixed boundary used to make a binary decision. Standard error describes variability, and degrees of freedom affect the distribution and thus the critical value, but neither is the boundary itself.

Critical value is the boundary that separates significant from non-significant outcomes. In hypothesis testing you set a level of risk, called alpha (often 0.05), and compute a test statistic from your data. The critical value is the cutoff on that statistic derived from the chosen distribution. If the test statistic falls beyond this cutoff—outside the central region for a two-tailed test or in the tail for a one-tailed test—you reject the null hypothesis and declare significance. The exact cutoff depends on the test type (z, t, chi-square) and, for t tests, on degrees of freedom. The p-value provides another way to gauge significance, but the critical value is the fixed boundary used to make a binary decision. Standard error describes variability, and degrees of freedom affect the distribution and thus the critical value, but neither is the boundary itself.