When presented with a statistical study, it’s important to consider the significance of the results. Here’s a simple guide to help you interpret the statistical and practical significance.
- Understand the context: Think about the question the study is trying to answer and the population it’s trying to generalize to. For example, a study may be looking at whether a new drug is effective in reducing symptoms of a medical condition.
- Sample size: Check the number of participants in the study. A larger sample size usually leads to more accurate and reliable results. Imagine trying to predict the outcome of an election based on the opinions of just 10 people versus 1,000 people. The larger sample will give you a better idea of the overall sentiment.
- Effect size: This is a measure of the magnitude of the difference or relationship observed in the study. For example, if a weight loss program claims to help participants lose 10 pounds on average, that’s the effect size. The larger the effect size, the more likely it is to be practically significant.
- Statistical significance: This refers to the probability that the observed effect is due to chance. Usually, a p-value (probability value) of less than 0.05 is considered statistically significant. This means that there is a 5% chance or less that the observed effect is due to random chance. In our weight loss program example, a p-value less than 0.05 would indicate that the program is likely effective.
- Confidence intervals: These provide a range of values within which the true effect size is likely to fall. For example, if a study reports a 95% confidence interval for the average weight loss as 8 to 12 pounds, it means we can be 95% confident that the true average weight loss falls within that range.
- External validity: Consider whether the study’s results can be applied to other situations or populations. For example, a study conducted on a specific age group may not be applicable to everyone.
- Practical significance: This relates to the real-world impact of the study’s findings. Even if a study is statistically significant, it may not be practically significant. For instance, if a medication reduces the duration of a common cold by just 30 minutes, it might not be worth the cost or potential side effects.
To sum up, when evaluating a statistical study, consider the context, sample size, effect size, statistical significance, confidence intervals, external validity, and practical significance. This will help you make more informed decisions when consuming statistical information.