When should the null hypothesis be rejected based on p-value?

When should the null hypothesis be rejected based on p-value?

• A. When it is greater than 0.10
• B. When it is less than or equal to 0.05
• C. When it equals 0.05
• D. When it is close to 1.0

Answer: (B)

The null hypothesis should be rejected when the p-value is less than or equal to 0.05 because this threshold indicates a statistically significant result. In hypothesis testing, the p-value represents the probability of obtaining the observed results, or something more extreme, assuming the null hypothesis is true. If the p-value is below the chosen significance level (often set at 0.05), it suggests that the observed data is unlikely under the null hypothesis, thus providing sufficient evidence to reject it. Choosing a p-value threshold of 0.05 demonstrates a balance between minimizing false positives and ensuring sufficient power to detect an effect if it exists. A p-value less than or equal to 0.05 implies that there is strong evidence against the null hypothesis, leading researchers to conclude that there is likely a statistically significant effect present in the data being studied.

The null hypothesis should be rejected when the p-value is less than or equal to 0.05 because this threshold indicates a statistically significant result. In hypothesis testing, the p-value represents the probability of obtaining the observed results, or something more extreme, assuming the null hypothesis is true. If the p-value is below the chosen significance level (often set at 0.05), it suggests that the observed data is unlikely under the null hypothesis, thus providing sufficient evidence to reject it.

Choosing a p-value threshold of 0.05 demonstrates a balance between minimizing false positives and ensuring sufficient power to detect an effect if it exists. A p-value less than or equal to 0.05 implies that there is strong evidence against the null hypothesis, leading researchers to conclude that there is likely a statistically significant effect present in the data being studied.