Effect sizes and confidence intervals US Medical PG Practice Questions and MCQs
Practice US Medical PG questions for Effect sizes and confidence intervals. These multiple choice questions (MCQs) cover important concepts and help you prepare for your exams.
Effect sizes and confidence intervals US Medical PG Question 1: Group of 100 medical students took an end of the year exam. The mean score on the exam was 70%, with a standard deviation of 25%. The professor states that a student's score must be within the 95% confidence interval of the mean to pass the exam. Which of the following is the minimum score a student can have to pass the exam?
- A. 45%
- B. 63.75%
- C. 67.5%
- D. 20%
- E. 65% (Correct Answer)
Effect sizes and confidence intervals Explanation: ***65%***
- To find the **95% confidence interval (CI) of the mean**, we use the formula: Mean ± (Z-score × Standard Error). For a 95% CI, the Z-score is approximately **1.96**.
- The **Standard Error (SE)** is calculated as SD/√n, where n is the sample size (100 students). So, SE = 25%/√100 = 25%/10 = **2.5%**.
- The 95% CI is 70% ± (1.96 × 2.5%) = 70% ± 4.9%. The lower bound is 70% - 4.9% = **65.1%**, which rounds to **65%** as the minimum passing score.
*45%*
- This value is significantly lower than the calculated lower bound of the 95% confidence interval (approximately 65.1%).
- It would represent a score far outside the defined passing range.
*63.75%*
- This value falls below the calculated lower bound of the 95% confidence interval (approximately 65.1%).
- While close, this score would not meet the professor's criterion for passing.
*67.5%*
- This value is within the 95% confidence interval (65.1% to 74.9%) but is **not the minimum score**.
- Lower scores within the interval would still qualify as passing.
*20%*
- This score is extremely low and falls significantly outside the 95% confidence interval for a mean of 70%.
- It would indicate performance far below the defined passing threshold.
Effect sizes and confidence intervals US Medical PG Question 2: You are conducting a study comparing the efficacy of two different statin medications. Two groups are placed on different statin medications, statin A and statin B. Baseline LDL levels are drawn for each group and are subsequently measured every 3 months for 1 year. Average baseline LDL levels for each group were identical. The group receiving statin A exhibited an 11 mg/dL greater reduction in LDL in comparison to the statin B group. Your statistical analysis reports a p-value of 0.052. Which of the following best describes the meaning of this p-value?
- A. There is a 95% chance that the difference in reduction of LDL observed reflects a real difference between the two groups
- B. Though A is more effective than B, there is a 5% chance the difference in reduction of LDL between the two groups is due to chance
- C. If 100 permutations of this experiment were conducted, 5 of them would show similar results to those described above
- D. This is a statistically significant result
- E. There is a 5.2% chance of observing a difference in reduction of LDL of 11 mg/dL or greater even if the two medications have identical effects (Correct Answer)
Effect sizes and confidence intervals Explanation: **There is a 5.2% chance of observing a difference in reduction of LDL of 11 mg/dL or greater even if the two medications have identical effects**
- The **p-value** represents the probability of observing results as extreme as, or more extreme than, the observed data, assuming the **null hypothesis** is true (i.e., there is no true difference between the groups).
- A p-value of 0.052 means there's approximately a **5.2% chance** that the observed 11 mg/dL difference (or a more substantial difference) occurred due to **random variation**, even if both statins were equally effective.
*There is a 95% chance that the difference in reduction of LDL observed reflects a real difference between the two groups*
- This statement is an incorrect interpretation of the p-value; it confuses the p-value with the **probability that the alternative hypothesis is true**.
- A p-value does not directly tell us the probability that the observed difference is "real" or due to the intervention being studied.
*Though A is more effective than B, there is a 5% chance the difference in reduction of LDL between the two groups is due to chance*
- This statement implies that Statin A is more effective, which cannot be concluded with a p-value of 0.052 if the significance level (alpha) was set at 0.05.
- While it's true there's a chance the difference is due to chance, claiming A is "more effective" based on this p-value before statistical significance is usually declared is misleading.
*If 100 permutations of this experiment were conducted, 5 of them would show similar results to those described above*
- This is an incorrect interpretation. The p-value does not predict the outcome of repeated experiments in this manner.
- It refers to the **probability under the null hypothesis in a single experiment**, not the frequency of results across multiple hypothetical repetitions.
*This is a statistically significant result*
- A p-value of 0.052 is generally considered **not statistically significant** if the conventional alpha level (significance level) is set at 0.05 (or 5%).
- For a result to be statistically significant at alpha = 0.05, the p-value must be **less than 0.05**.
Effect sizes and confidence intervals US Medical PG Question 3: An investigator is studying the effect of antihypertensive drugs on cardiac output and renal blood flow. For comparison, a healthy volunteer is given a placebo and a continuous infusion of para-aminohippuric acid (PAH) to achieve a plasma concentration of 0.02 mg/ml. His urinary flow rate is 1.5 ml/min and the urinary concentration of PAH is measured to be 8 mg/ml. His hematocrit is 50%. Which of the following values best estimates cardiac output in this volunteer?
- A. 8 L/min
- B. 3 L/min
- C. 4 L/min
- D. 1.2 L/min
- E. 6 L/min (Correct Answer)
Effect sizes and confidence intervals Explanation: ***6 L/min***
- This value represents the estimated **cardiac output** based on the calculated renal blood flow.
- Step 1: Calculate renal plasma flow (RPF) using PAH clearance: RPF = (Urinary PAH × Urine flow rate) / Plasma PAH = (8 mg/ml × 1.5 ml/min) / 0.02 mg/ml = 600 ml/min = 0.6 L/min
- Step 2: Calculate renal blood flow (RBF): Since hematocrit is 50%, RBF = RPF / (1 - Hematocrit) = 0.6 / 0.5 = 1.2 L/min
- Step 3: Estimate cardiac output: The kidneys normally receive approximately **20-25% of cardiac output**. Using 20%: Cardiac Output = RBF / 0.20 = 1.2 / 0.20 = **6 L/min**
- This is consistent with normal resting cardiac output in a healthy adult.
*8 L/min*
- This value overestimates cardiac output based on the renal blood flow calculation.
- While some individuals may have higher cardiac output during exercise, the calculated RBF of 1.2 L/min suggests a resting cardiac output closer to 6 L/min.
*3 L/min*
- This value significantly underestimates cardiac output.
- If cardiac output were 3 L/min, the kidneys would be receiving 40% of cardiac output (1.2/3), which is physiologically implausible at rest.
*4 L/min*
- This value underestimates cardiac output based on the renal data.
- This would mean kidneys receive 30% of cardiac output (1.2/4), which is higher than the typical 20-25%.
*1.2 L/min*
- This is the calculated **renal blood flow**, not cardiac output.
- While this calculation is correct for RBF, the question specifically asks for cardiac output estimation, which requires accounting for the fact that kidneys receive only about 20-25% of total cardiac output.
Effect sizes and confidence intervals US Medical PG Question 4: An epidemiologist is evaluating the efficacy of Noxbinle in preventing HCC deaths at the population level. A clinical trial shows that over 5 years, the mortality rate from HCC was 25% in the control group and 15% in patients treated with Noxbinle 100 mg daily. Based on this data, how many patients need to be treated with Noxbinle 100 mg to prevent, on average, one death from HCC?
- A. 20
- B. 73
- C. 10 (Correct Answer)
- D. 50
- E. 100
Effect sizes and confidence intervals Explanation: ***10***
- The **number needed to treat (NNT)** is calculated by first finding the **absolute risk reduction (ARR)**.
- **ARR** = Risk in control group - Risk in treatment group = 25% - 15% = **10%** (or 0.10).
- **NNT = 1 / ARR** = 1 / 0.10 = **10 patients**.
- This means that **10 patients must be treated with Noxbinle to prevent one death from HCC** over 5 years.
*20*
- This would result from an ARR of 5% (1/0.05 = 20), which is not supported by the data.
- May arise from miscalculating the risk difference or incorrectly halving the actual ARR.
*73*
- This value does not correspond to any standard calculation of NNT from the given mortality rates.
- May result from confusion with other epidemiological measures or calculation error.
*50*
- This would correspond to an ARR of 2% (1/0.02 = 50), which significantly underestimates the actual risk reduction.
- Could result from incorrectly calculating the difference as a proportion rather than absolute percentage points.
*100*
- This would correspond to an ARR of 1% (1/0.01 = 100), grossly underestimating the treatment benefit.
- May result from confusing ARR with relative risk reduction or other calculation errors.
Effect sizes and confidence intervals US Medical PG Question 5: A researcher is examining the relationship between socioeconomic status and IQ scores. The IQ scores of young American adults have historically been reported to be distributed normally with a mean of 100 and a standard deviation of 15. Initially, the researcher obtains a random sampling of 300 high school students from public schools nationwide and conducts IQ tests on all participants. Recently, the researcher received additional funding to enable an increase in sample size to 2,000 participants. Assuming that all other study conditions are held constant, which of the following is most likely to occur as a result of this additional funding?
- A. Increase in risk of systematic error
- B. Increase in range of the confidence interval
- C. Decrease in standard deviation
- D. Increase in probability of type II error
- E. Decrease in standard error of the mean (Correct Answer)
Effect sizes and confidence intervals Explanation: ***Decrease in standard error of the mean***
- **Increasing the sample size** (n) leads to a **decrease in the standard error of the mean** (SEM), which is calculated as σ/√n.
- A smaller SEM indicates that our sample mean is a more **precise estimate** of the true population mean.
*Increase in risk of systematic error*
- **Systematic error** is related to flaws in study design or implementation and is not directly affected by an increase in sample size.
- A larger sample size generally helps in detecting a true effect if one exists, but does not inherently introduce or correct systematic bias.
*Increase in range of the confidence interval*
- An **increase in sample size** typically leads to a **narrower confidence interval**, not a wider one, because the standard error of the mean decreases.
- A narrower confidence interval implies greater precision in estimating the population parameter.
*Decrease in standard deviation*
- The **standard deviation** is a measure of the data's spread within a sample or population and is an intrinsic characteristic of the data itself.
- Increasing the sample size typically does not change the true standard deviation of the population; it only provides a **more accurate estimate** of it.
*Increase in probability of type II error*
- An **increase in sample size** generally leads to an **increase in statistical power**, which in turn **decreases the probability of a Type II error** (failing to reject a false null hypothesis).
- A larger sample makes it easier to detect a true difference or effect if one exists.
Effect sizes and confidence intervals US Medical PG Question 6: You submit a paper to a prestigious journal about the effects of coffee consumption on mesothelioma risk. The first reviewer lauds your clinical and scientific acumen, but expresses concern that your study does not have adequate statistical power. Statistical power refers to which of the following?
- A. The probability of detecting an association when no association exists.
- B. The probability of not detecting an association when an association does exist.
- C. The probability of detecting an association when an association does exist. (Correct Answer)
- D. The first derivative of work.
- E. The square root of the variance.
Effect sizes and confidence intervals Explanation: ***The probability of detecting an association when an association does exist.***
- **Statistical power** is defined as the probability that a study will correctly reject a false null hypothesis, meaning it will detect a true effect or association if one exists.
- A study with **adequate statistical power** is less likely to miss a real effect.
*The probability of detecting an association when no association exists.*
- This describes a **Type I error** or **false positive**, often represented by **alpha (α)**.
- It is the probability of incorrectly concluding an effect or association exists when, in reality, there is none.
*The probability of not detecting an association when an association does exist.*
- This refers to a **Type II error** or **false negative**, represented by **beta (β)**.
- **Statistical power** is calculated as **1 - β**, so this option describes the complement of power.
*The first derivative of work.*
- The first derivative of work with respect to time represents **power** in physics, which is the rate at which work is done.
- This option is a **distractor** from physics and is unrelated to statistical power in research.
*The square root of the variance.*
- The **square root of the variance** is the **standard deviation**, a measure of the dispersion or spread of data.
- This is a statistical concept but is not the definition of statistical power.
Effect sizes and confidence intervals US Medical PG Question 7: A 28-year-old male presents to his primary care physician with complaints of intermittent abdominal pain and alternating bouts of constipation and diarrhea. His medical chart is not significant for any past medical problems or prior surgeries. He is not prescribed any current medications. Which of the following questions would be the most useful next question in eliciting further history from this patient?
- A. "Does the diarrhea typically precede the constipation, or vice-versa?"
- B. "Is the diarrhea foul-smelling?"
- C. "Please rate your abdominal pain on a scale of 1-10, with 10 being the worst pain of your life"
- D. "Are the symptoms worse in the morning or at night?"
- E. "Can you tell me more about the symptoms you have been experiencing?" (Correct Answer)
Effect sizes and confidence intervals Explanation: ***Can you tell me more about the symptoms you have been experiencing?***
- This **open-ended question** encourages the patient to provide a **comprehensive narrative** of their symptoms, including details about onset, frequency, duration, alleviating/aggravating factors, and associated symptoms, which is crucial for diagnosis.
- In a patient presenting with vague, intermittent symptoms like alternating constipation and diarrhea, allowing them to elaborate freely can reveal important clues that might not be captured by more targeted questions.
*Does the diarrhea typically precede the constipation, or vice-versa?*
- While knowing the sequence of symptoms can be helpful in understanding the **pattern of bowel dysfunction**, it is a very specific question that might overlook other important aspects of the patient's experience.
- It prematurely narrows the focus without first obtaining a broad understanding of the patient's overall symptomatic picture.
*Is the diarrhea foul-smelling?*
- Foul-smelling diarrhea can indicate **malabsorption** or **bacterial overgrowth**, which are important to consider in some gastrointestinal conditions.
- However, this is a **specific symptom inquiry** that should follow a more general exploration of the patient's symptoms, as it may not be relevant if other crucial details are missed.
*Please rate your abdominal pain on a scale of 1-10, with 10 being the worst pain of your life*
- Quantifying pain intensity is useful for assessing the **severity of discomfort** and monitoring changes over time.
- However, for a patient with intermittent rather than acute, severe pain, understanding the **character, location, and triggers** of the pain is often more diagnostically valuable than just a numerical rating initially.
*Are the symptoms worse in the morning or at night?*
- Diurnal variation can be relevant in certain conditions, such as inflammatory bowel diseases where nocturnal symptoms might be more concerning, or functional disorders whose symptoms might be stress-related.
- This is another **specific question** that should come after gathering a more complete initial picture of the patient's symptoms to ensure no key information is overlooked.
Effect sizes and confidence intervals US Medical PG Question 8: A researcher is investigating the effects of a new antihypertensive medication on renal physiology. She gives a subject a dose of the new medication, and she then collects plasma and urine samples. She finds the following: Hematocrit: 40%; Serum creatinine: 0.0125 mg/mL; Urine creatinine: 1.25 mg/mL. Urinary output is 1 mL/min. Renal blood flow is 1 L/min. Based on the above information and approximating that the creatinine clearance is equal to the GFR, what answer best approximates filtration fraction in this case?
- A. 10%
- B. 17% (Correct Answer)
- C. 33%
- D. 50%
- E. 25%
Effect sizes and confidence intervals Explanation: ***17%***
- First, calculate **GFR** using the creatinine clearance formula: GFR = (Urine creatinine × Urinary output) / Serum creatinine = (1.25 mg/mL × 1 mL/min) / 0.0125 mg/mL = **100 mL/min**.
- Next, calculate **Renal Plasma Flow (RPF)** from Renal Blood Flow (RBF) and Hematocrit: RPF = RBF × (1 - Hematocrit) = 1000 mL/min × (1 - 0.40) = **600 mL/min**.
- Finally, calculate **Filtration Fraction (FF)** = GFR / RPF = 100 mL/min / 600 mL/min = 0.1667 = **16.7%, which approximates to 17%**.
- This is the correct answer based on the physiological calculations and represents a normal filtration fraction.
*10%*
- This would correspond to a filtration fraction of 0.10, which would require either a GFR of 60 mL/min (lower than calculated) or an RPF of 1000 mL/min (higher than calculated).
- This value is too low given the provided parameters and doesn't match the calculation from the given data.
*25%*
- This value would suggest FF = 0.25, requiring a GFR of 150 mL/min with the calculated RPF of 600 mL/min.
- This is higher than the calculated GFR of 100 mL/min and doesn't match the given creatinine values.
*33%*
- This would imply FF = 0.33, requiring a GFR of approximately 200 mL/min with RPF of 600 mL/min.
- This is significantly higher than the calculated GFR and would represent an abnormally elevated filtration fraction.
*50%*
- A filtration fraction of 50% is unphysiologically high and would indicate severe pathology.
- This would require a GFR of 300 mL/min with the calculated RPF, which is impossible given the provided creatinine clearance data.
Effect sizes and confidence intervals US Medical PG Question 9: You are interested in studying the etiology of heart failure reduced ejection fraction (HFrEF) and attempt to construct an appropriate design study. Specifically, you wish to look for potential causality between dietary glucose consumption and HFrEF. Which of the following study designs would allow you to assess for and determine this causality?
- A. Cross-sectional study
- B. Case series
- C. Cohort study (Correct Answer)
- D. Case-control study
- E. Randomized controlled trial
Effect sizes and confidence intervals Explanation: ***Cohort study***
- A **cohort study** observes a group of individuals over time to identify risk factors and outcomes, allowing for the assessment of **temporal relationships** between exposure (dietary glucose) and outcome (HFrEF).
- This design is suitable for establishing a potential **causal link** as it tracks participants from exposure to outcome, enabling the calculation of incidence rates and relative risks.
*Cross-sectional study*
- A **cross-sectional study** measures exposure and outcome simultaneously at a single point in time, making it impossible to determine the **temporal sequence** of events.
- This design can only identify **associations** or correlations, not causation, as it cannot establish whether high glucose consumption preceded HFrEF.
*Case series*
- A **case series** describes characteristics of a group of patients with a particular disease or exposure, often to highlight unusual clinical features, but it lacks a **comparison group**.
- It cannot assess causality because it does not provide information on the frequency of exposure in healthy individuals or the incidence of the disease in unexposed individuals.
*Case-control study*
- A **case-control study** compares individuals with the outcome (cases) to those without the outcome (controls) to determine past exposures, which makes it prone to **recall bias**.
- While it can suggest associations, it cannot definitively establish a temporal relationship or causation as the outcome is already known when exposure is assessed.
*Randomized controlled trial*
- A **randomized controlled trial (RCT)** is the gold standard for establishing causation by randomly assigning participants to an intervention or control group, but it may not be ethical or feasible for studying long-term dietary exposures and chronic diseases like HFrEF due to the long follow-up period and complexity of diet.
- While ideal for causality, directly controlling and randomizing dietary glucose intake over decades to observe HFrEF development might be practically challenging or unethical.
Effect sizes and confidence intervals US Medical PG Question 10: A 54-year-old man is brought to the emergency department 30 minutes after being hit by a car while crossing the street. He had a left-sided tonic-clonic seizure and one episode of vomiting while being transported to the hospital. On arrival, he is not oriented to person, place, or time. Physical examination shows flaccid paralysis of all extremities. A CT scan of the head is shown. This patient's symptoms are most likely the result of a hemorrhage in which of the following structures?
- A. Between the dura mater and the arachnoid mater
- B. Into the cerebral parenchyma
- C. Between the skull and the dura mater
- D. Between the arachnoid mater and the pia mater (Correct Answer)
- E. Into the ventricular system
Effect sizes and confidence intervals Explanation: ***Between the arachnoid mater and the pia mater (Correct)***
- The CT scan demonstrates diffuse high-density (white) material within the sulci and basal cisterns, indicative of a **subarachnoid hemorrhage**. This space is located between the arachnoid mater and the pia mater.
- The patient's presentation with altered mental status, seizures, vomiting, and flaccid paralysis following trauma is consistent with the severe neurological impact of a **traumatic subarachnoid hemorrhage**.
*Between the dura mater and the arachnoid mater (Incorrect)*
- Hemorrhage in this location is known as a **subdural hematoma**, which typically appears as a crescent-shaped collection of blood.
- While possible in trauma, the CT image shows blood primarily filling the sulci, not a subdural collection.
*Into the cerebral parenchyma (Incorrect)*
- This would be an **intraparenchymal hemorrhage**, appearing as a focal area of high density within the brain tissue itself.
- Although there might be some associated parenchymal injury in severe trauma, the predominant pattern seen on the CT is diffuse blood in the subarachnoid space.
*Between the skull and the dura mater (Incorrect)*
- This describes an **epidural hematoma**, often characterized by a lenticular (lens-shaped) collection of blood due to its confinement by dural attachments.
- The CT image does not show a lenticular collection of blood in this space.
*Into the ventricular system (Incorrect)*
- **Intraventricular hemorrhage** would show blood filling the cerebral ventricles.
- While subarachnoid hemorrhage can sometimes extend into the ventricles, the primary finding on this CT is diffuse blood in the subarachnoid space, not isolated ventricular blood.
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