When is P Value Significant?

A p-value below 0.05 often signals significant results, questioning the null hypothesis's validity.

Represents the probability of observing data as extreme, or more so, if the null hypothesis holds true.

Alpha, typically at 0.05, sets the bar for significance, balancing the risk of Type I errors.

Occurs when a true null hypothesis is wrongly rejected, a critical consideration in hypothesis testing.

Larger samples enhance the test's power, making it more likely to detect true, even subtle, effects.

Indicates the practical significance of the observed difference, crucial for interpreting results.

A common error is viewing a low p-value as direct evidence against the null hypothesis.

Reflects the likelihood of data extremity under the null hypothesis, not its probability of truth.

The 0.05 standard is not universal; significance levels should be tailored to the study's context.

Consideration of effect size and confidence intervals provides a fuller picture of research findings.