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Zorluk: OrtaStatistical Inference and Hypothesis Testing

An academic medical center evaluates a new electronic health record (EHR) alert designed to reduce inappropriate broad-spectrum antibiotic prescriptions for uncomplicated acute bronchitis. The researchers conduct a prospective, randomized controlled trial by randomizing N=40N = 40 outpatient physicians to either the EHR alert group or the standard workflow group. Over six months, the data show that physicians in the intervention group had an 18% greater reduction in broad-spectrum antibiotic prescriptions compared to the control group. Statistical analysis yields a pp-value of 0.11 and a 95% confidence interval of -38% to +2%. The researchers conclude that the EHR alert does not significantly reduce prescription rates. Which of the following best explains why the study failed to demonstrate statistical significance despite a potentially meaningful clinical difference?

  1. Inadequate statistical power leading to a Type II errorCevap
  2. B
    Confounding by patient comorbidities skewing the results
  3. C
    Decreased positive predictive value of the bronchitis diagnosis
  4. D
    Inability to determine causality due to the observational study design
  5. E
    Provider failure to utilize narrow-spectrum antibiotics based on culture data

Cevap

Inadequate statistical power leading to a Type II error
The correct answer identifies that the study likely suffered from a Type II error (β\beta error), which occurs when a study fails to reject a null hypothesis that is actually false (i.e., missing a true difference). This is most commonly caused by an inadequate sample size, resulting in low statistical power (1−β1 - \beta). In this scenario, despite a potentially meaningful observed clinical difference (18% reduction), the small sample size (N=40N = 40) resulted in a wide confidence interval that crosses the null value of 0, yielding a non-significant pp-value (>0.05> 0.05). The investigators may have missed a true benefit of the EHR alert because the study was underpowered.

Adım Adım Çözüm

1
Analyze the observed results and statistical values.
The study found an 18% difference, but the pp-value is 0.11 (greater than 0.05) and the 95% CI crosses zero (-38% to +2%).
This indicates that the finding is not statistically significant, and the null hypothesis cannot be rejected.
2
Evaluate the study design parameters that influence significance.
The sample size is small (N=40N = 40 physicians).
Sample size is a primary determinant of the width of a confidence interval and the resulting pp-value.
3
Connect the findings to the correct statistical principle.
A small sample size leads to low statistical power, meaning the study is unable to reliably detect a true difference even if one exists.
Failing to reject a false null hypothesis due to low power is the definition of a Type II (β\beta) error.

Anahtar Kavram

Type II error (β\beta) occurs when a study fails to reject a false null hypothesis, often due to low statistical power driven by an inadequate sample size.
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Konular

Statistical Inference and Hypothesis Testing

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