Review of Measures of Statistical Significance and Effect Size

In biostatistics, two commonly used measures of statistical significance and effect size are the p-value and the odds ratio (OR). These measures provide important information about the strength of a relationship between two variables, and can be used to make important decisions in clinical and research settings. This article will provide an overview of these measures, their interpretation, and their use in biostatistics.

P-Value

The p-value is a measure of the probability that the observed result was due to chance. Specifically, it is the probability of obtaining a result as extreme as, or more extreme than, the observed result if the null hypothesis is true. The null hypothesis is usually a statement of no effect or no difference between two groups. The smaller the p-value, the more likely it is that the observed result was not due to chance.

The p-value is commonly used in biostatistics to determine whether an observed effect is statistically significant. A “significant” p-value is usually defined as 5% or less. This means that if the probability of obtaining the observed result is 5% or less, then the result is considered statistically significant, and the null hypothesis is rejected.

Odds Ratio

An odds ratio (OR) is a measure of effect size that is used to compare the odds of an event occurring in one group to the odds of the same event occurring in another group. The OR is a ratio of two odds, and is usually expressed as a value greater than 1. A value of 1 indicates that the odds of the event occurring in the two groups are equal, and the greater the value, the greater the difference in the odds of the event occurring in the two groups.

An OR can be used to measure the strength of the association between two variables. Generally, an OR of 1.5 or greater is considered a strong association, and an OR of 3 or greater is considered a very strong association. However, it is important to note that the interpretation of an OR depends on the context of the study.

Interpretation of Statistical Significance and Effect Size

When interpreting the results of a study, it is important to consider both the p-value and the OR. The p-value is used to determine whether the observed result is statistically significant, and the OR is used to determine the strength of the association between two variables.

In general, if the p-value is significant (less than 5%) and the OR is strong (greater than 1.5), then the observed result is considered to be true and meaningful. On the other hand, if the p-value is not significant (greater than 5%) and/or the OR is weak (less than 1.5), then the observed result may not be true or meaningful, and further study may be necessary.

Use of Statistical Significance and Effect Size in Biostatistics

In biostatistics, measures of statistical significance and effect size are used to make important decisions in clinical and research settings. For example, if a study finds that a particular treatment has a statistically significant effect on a patient’s condition (p-value < 0.05) and a strong effect size (OR > 1.5), then the treatment is likely to be effective and should be considered for use in the patient’s care. On the other hand, if the study finds that the treatment has a weak effect size (OR < 1.5) and/or a non-significant result (p-value > 0.05), then the treatment should not be used, and further study may be necessary.

Additionally, measures of statistical significance and effect size can be used to compare the effectiveness of different treatments. For example, if one treatment has a statistically significant result (p-value < 0.05) and a strong effect size (OR > 1.5), and another treatment has a non-significant result (p-value > 0.05) and/or a weak effect size (OR < 1.5), then the first treatment is likely to be more effective and should be given preference.

Conclusion

In conclusion, measures of statistical significance and effect size are important measures in biostatistics that can be used to make important decisions in clinical and research settings. The p-value is used to determine whether an observed effect is statistically significant, and the OR is used to determine the strength of the association between two variables. When interpreting the results of a study, it is important to consider both the p-value and the OR. If the p-value is significant (less than 5%) and the OR is strong (greater than 1.5), then the observed result is considered to be true and meaningful. On the other hand, if the p-value is not significant (greater than 5%) and/or the OR is weak (less than 1.5), then the observed result may not be true or meaningful, and further study may be necessary.