What statistical approach helps determine if observed CDI metric changes are meaningful?

• A. Descriptive statistics
• B. Hypothesis testing with confidence intervals
• C. Qualitative assessment
• D. Time-series forecasting

Answer: (B)

Inferring whether a CDI metric change is real requires inferential statistics rather than purely descriptive data. Descriptive statistics summarize what happened, but they don’t tell you whether the change could occur by random variation. This is where hypothesis testing comes in: you set a null hypothesis that there’s no change, choose a significance level, and use a statistical test to see if the observed change is unlikely under that null. If the result is unlikely, you conclude the change is meaningful beyond random fluctuations.

Confidence intervals complement this by showing the range of values for the true change with a stated level of certainty. If the interval for the change does not include zero, it indicates a statistically meaningful difference and also provides an estimate of how large the change might be, with its precision. Together, hypothesis testing and confidence intervals offer both a decision about significance and an estimate of effect size.

Other approaches like qualitative assessments don’t quantify statistical significance, and time-series forecasting focuses on predicting future values rather than testing whether the observed current change is due to real difference rather than chance.