Statistical Significance

Definition

Statistical significance is a statistical measure that determines the probability of an observed effect or relationship occurring by chance alone. It is a crucial concept in research and data analysis, as it helps researchers evaluate the reliability and validity of their findings.

5 Must Know Facts For Your Next Test

1. Statistical significance is typically set at a p-value of 0.05, meaning there is a 5% or less chance that the observed effect occurred by chance.
2. A statistically significant result does not necessarily imply practical or clinical significance, as the effect size must also be considered.
3. The choice of statistical test and the assumptions of the test (e.g., normality, homogeneity of variance) can affect the determination of statistical significance.
4. Larger sample sizes generally increase the power to detect statistically significant effects, even if the effect size is small.
5. Statistical significance is a necessary but not sufficient condition for establishing a causal relationship between variables; other criteria, such as temporal precedence and plausibility, must also be considered.

Review Questions

• Explain how the concept of statistical significance is used to evaluate the reliability and validity of research findings.
  • Statistical significance is used to determine the probability that the observed results in a research study occurred by chance alone. A low p-value (typically less than 0.05) indicates that the observed effect or relationship is unlikely to have occurred by chance, providing evidence to reject the null hypothesis and suggesting that the findings are reliable and valid. This allows researchers to have confidence in the conclusions drawn from their data and make informed decisions about the practical implications of their work.
• Describe the relationship between statistical significance, effect size, and the practical or clinical relevance of research findings.
  • While statistical significance indicates the likelihood that an observed effect or relationship is not due to chance, it does not necessarily reflect the practical or clinical significance of the findings. Effect size is a measure of the magnitude of the observed effect, which provides information about the practical importance of the results. A statistically significant finding with a small effect size may have limited practical relevance, whereas a finding with a large effect size may be more impactful, even if it does not reach the traditional threshold for statistical significance. Researchers must consider both statistical significance and effect size when evaluating the overall importance and implications of their research.
• Analyze how factors such as sample size, test assumptions, and the choice of statistical test can influence the determination of statistical significance.
  • The determination of statistical significance can be affected by various factors, including sample size, test assumptions, and the choice of statistical test. Larger sample sizes generally increase the power to detect statistically significant effects, even if the effect size is small. However, if the assumptions of the statistical test (e.g., normality, homogeneity of variance) are violated, the results may be misleading and the determination of statistical significance may be inaccurate. The choice of statistical test can also impact the assessment of statistical significance, as different tests have different strengths, weaknesses, and underlying assumptions. Researchers must carefully consider these factors when designing their studies and interpreting their findings to ensure the reliability and validity of their conclusions.

Related terms

p-value:

The p-value is the probability of obtaining the observed or more extreme results if the null hypothesis (that there is no effect or relationship) is true. A low p-value indicates that the observed effect is unlikely to have occurred by chance.

Null Hypothesis: The null hypothesis is a statement that there is no significant difference or relationship between the variables being studied. Statistical significance is used to determine whether the observed data provides enough evidence to reject the null hypothesis.

Effect Size:

The effect size is a measure of the magnitude of the observed effect or relationship, independent of sample size. It provides information about the practical significance of the findings, in addition to the statistical significance.