What is the conventional threshold p-value used to define statistical significance?

What is the conventional threshold p-value used to define statistical significance?

• A. 0.05
• B. 0.01
• C. 0.10
• D. 0.5

Answer: (A)

p-values tell us how compatible the observed data are with the null hypothesis, and the conventional threshold for calling a result statistically significant is 0.05. That means if the null is true, there’s about a 5% chance of obtaining data as extreme or more extreme than what we observed. When the p-value is at or below 0.05, we reject the null with standard confidence, accepting a 5% risk of a false positive. This 5% cutoff is a long-standing convention that balances detecting real effects with limiting false alarms, though some fields use stricter criteria like 0.01, especially when the costs of a false positive are high. A p-value of 0.10 would not meet the standard threshold and is not considered statistically significant, while 0.5 indicates the data are quite consistent with the null and provide little evidence against it.

p-values tell us how compatible the observed data are with the null hypothesis, and the conventional threshold for calling a result statistically significant is 0.05. That means if the null is true, there’s about a 5% chance of obtaining data as extreme or more extreme than what we observed. When the p-value is at or below 0.05, we reject the null with standard confidence, accepting a 5% risk of a false positive. This 5% cutoff is a long-standing convention that balances detecting real effects with limiting false alarms, though some fields use stricter criteria like 0.01, especially when the costs of a false positive are high. A p-value of 0.10 would not meet the standard threshold and is not considered statistically significant, while 0.5 indicates the data are quite consistent with the null and provide little evidence against it.