EPPP: Part 1, Knowledge Quiz: Statistical Interpretation

20 questions · exam conditions

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Statistical InterpretationQuestion 1 of 20

A study plans $80\%$ power to detect $d=0.40$ ; how does statistical power influence interpretation of research findings?

It is the probability that the null hypothesis is true after data collection.

It guarantees statistical significance if the intervention is clinically meaningful.

It is only the number of participants, so effect size assumptions are irrelevant.

It reflects the study's ability to detect the targeted effect, shaping confidence in null vs non-null results.

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EPPP: Part 1, Knowledge Quiz

EPPP: Part 1, Knowledge Quiz: Statistical Interpretation

Practice Statistical Interpretation in EPPP: Part 1, Knowledge with focused quiz questions that help you check what you know, review explanations, and build confidence with test-style prompts.

What this quiz covers

This quiz focuses on Statistical Interpretation, giving you a quick way to practice the rules, question types, and explanations that matter most for EPPP: Part 1, Knowledge.

How to use this quiz

Try each quiz question before looking at the correct answer. Use the explanations to review missed ideas, then come back to similar questions until the pattern feels familiar.

All questions

Question 1

A study plans $80\%$ power to detect $d=0.40$ ; how does statistical power influence interpretation of research findings?

It is the probability that the null hypothesis is true after data collection.
It guarantees statistical significance if the intervention is clinically meaningful.
It is only the number of participants, so effect size assumptions are irrelevant.
It reflects the study's ability to detect the targeted effect, shaping confidence in null vs non-null results. (correct answer)

Question 2

A parenting program yields $p=4.5\times10^{-2}$ ; which scenario best illustrates considering both effect size and power?

A large $N$ makes $p<.05$ , but $d$ is tiny; power is high, so practical value is questioned. (correct answer)
A small $N$ makes $p>.05$ , so the program is ineffective regardless of $d$ .
Only $p$ matters because it directly indicates clinical benefit.
Effect size always decides adoption, so power calculations are unnecessary.

Question 3

A large survey finds $p=1.0\times10^{-6}$ for a stress–health link, but $r=0.06$ ; why differentiate clinical and statistical significance?

Only $p$ -values are needed to determine whether to change health policy.
Clinical significance is the same as having $p<.05$ in any correlational study.
Clinical significance replaces the need to consider effect size in applied psychology.
Statistical significance can occur with trivial associations; clinical significance asks whether the link matters practically. (correct answer)

Question 4

A school intervention shows $p=1.0\times10^{-3}$ but tiny gains ( $d=0.08$ ); why differentiate clinical and statistical significance?

Statistical significance guarantees the change is noticeable to students and teachers.
Clinical significance assesses whether change is meaningful in real-world functioning, not just unlikely by chance. (correct answer)
Clinical significance is identical to having $p<.05$ in any sample.
Only $p$ -values matter for deciding whether to implement school programs.

Explanation: This question tests understanding of statistical interpretation concepts such as effect size, power, and clinical vs. statistical significance in psychological research. Effect size measures the magnitude of an effect, power determines the likelihood of detecting an effect if it exists, and clinical significance assesses the practical importance of findings. In this school intervention with p=0.001 but d=0.08, the highly significant p-value contrasts with the tiny effect, showing why practical meaning must be assessed separately. The correct answer explains that clinical significance assesses whether change is meaningful in real-world functioning, not just unlikely by chance. A common distractor assumes statistical significance guarantees noticeable changes, overlooking small effects in large samples. To assist learners, stress assessing both types of significance in educational interventions. Encourage critical evaluation of tiny effects despite low p-values.

Question 5

A depression trial ( $N=40$ ) reports $p=8.0\times10^{-2}$ ; how does statistical power influence interpretation of findings?

Power replaces the need to report effect sizes in psychological research.
Power is solely determined by participant count, not effect size or variability.
Power is the chance the null hypothesis will be accepted as true.
Low power can yield non-significant results even when a clinically meaningful effect exists. (correct answer)

Explanation: This question tests understanding of statistical interpretation concepts such as effect size, power, and clinical vs. statistical significance in psychological research. Effect size measures the magnitude of an effect, power determines the likelihood of detecting an effect if it exists, and clinical significance assesses the practical importance of findings. In this depression trial with N=40 and p=0.08, the non-significant result may stem from low power due to small sample size, potentially missing a real effect. The correct answer highlights that low power can yield non-significant results even when a clinically meaningful effect exists, affecting interpretation. A common distractor mistakenly views power as solely determined by participant count, ignoring effect size and variability. To assist learners, emphasize calculating power in study planning and interpretation. Encourage differentiating power's role from effect size in evaluating non-significant findings.

Question 6

A mindfulness study ( $N=500$ ) finds $p=3.0\times10^{-4}$ , $d=0.12$ ; what is the primary role of effect size?

It determines whether the null hypothesis is proven false beyond any doubt.
It is another name for statistical power when $N$ is large.
It reflects the practical magnitude of change, helping judge real-world importance. (correct answer)
It sets the minimum sample size needed without considering variability.

Explanation: This question tests understanding of statistical interpretation concepts such as effect size, power, and clinical vs. statistical significance in psychological research. Effect size measures the magnitude of an effect, power determines the likelihood of detecting an effect if it exists, and clinical significance assesses the practical importance of findings. In this mindfulness study with N=500, p=0.0003, and d=0.12, the small effect size despite strong significance illustrates the need to evaluate magnitude separately. The correct answer states that effect size reflects the practical magnitude of change, helping judge real-world importance. A common distractor confuses effect size with power or sample size requirements. To assist learners, practice interpreting effect sizes in large-N studies. Encourage focusing on magnitude beyond p-values for applied decisions.

Question 7

A prevention program yields $p=4.0\times10^{-2}$ with $d=0.10$ ; what is the primary role of effect size?

It determines the $\alpha$ level required to declare significance.
It shows whether results are statistically significant at $\alpha=5.0\times10^{-2}$ .
It is always comparable across all designs, so context is unnecessary.
It indicates the magnitude of the program's impact, independent of whether $p<.05$ . (correct answer)

Explanation: This question tests understanding of statistical interpretation concepts such as effect size, power, and clinical vs. statistical significance in psychological research. Effect size measures the magnitude of an effect, power determines the likelihood of detecting an effect if it exists, and clinical significance assesses the practical importance of findings. In this prevention program with p=0.04 and d=0.10, the scenario illustrates how a statistically significant result can have a very small effect, underscoring the need for effect size to evaluate real-world impact. The correct answer explains that effect size indicates the magnitude of the program's impact, independent of whether p<0.05, providing insight into practical value. A common distractor confuses effect size with determining statistical significance or alpha levels, overlooking its role in magnitude assessment. To assist learners, practice interpreting effect sizes in various study designs and contexts. Encourage evaluating research beyond p-values by integrating effect size for more comprehensive understanding.

Question 8

A treatment reduces panic attacks by 1 per month with $p=1.0\times10^{-2}$ ; explain clinical vs statistical significance difference.

Statistical significance reflects evidence against chance; clinical significance reflects whether reduction is meaningful to patients. (correct answer)
Clinical significance is determined by $p$ alone, regardless of patient functioning.
Clinical significance is the probability of detecting a true effect given the sample size.
Statistical significance reflects clinical improvement, while clinical significance reflects sampling error.

Question 9

A community study reports $p=4.0\times10^{-2}$ ; what is the primary role of effect size in psychological research?

It specifies the sampling method and ensures unbiased recruitment.
It determines whether the null hypothesis is accepted, replacing $p$ -values.
It is always unaffected by measurement quality, so it needs no context.
It indicates how strong the observed relationship or treatment impact is, supporting practical interpretation. (correct answer)

Question 10

A clinician chooses between interventions: one $d=0.45$ with low power; another $d=0.20$ with high power; which scenario best illustrates considering effect size and power?

Ignore both and decide using only whether $p<5.0\times10^{-2}$ .
Choose the low-power study because larger $d$ means nothing else matters.
Choose the high-power study because power alone determines clinical benefit.
Weigh larger estimated impact against uncertainty from low power, rather than relying on $p$ alone. (correct answer)

Question 11

A teen substance-use program shows $p=4.9\times10^{-2}$ with minimal behavior change; why is differentiating clinical and statistical significance crucial?

Statistical significance can reflect detectability, while clinical significance reflects meaningful behavior change for teens. (correct answer)
If $p<.05$ , clinical significance is guaranteed, so outcomes need no further interpretation.
Clinical significance is determined solely by randomization quality, not outcomes.
Clinical significance equals power, so larger samples always mean better clinical outcomes.

Question 12

A psychologist reads: $p=7.0\times10^{-2}$ , $d=0.50$ , power $=40\%$ ; which best interprets considering effect size and power?

Power is the same as $p$ , so reporting both is redundant.
Non-significance proves no effect, so effect size should be ignored.
Low power means the effect size is inflated by definition and must be exactly zero.
Moderate effect with low power suggests possible benefit; more data may clarify despite non-significance. (correct answer)

Question 13

A psychologist reviews results: $p=3.0\times10^{-2}$ , small $d=0.15$ , modest client change; why differentiate clinical and statistical significance?

To conclude that any statistically significant finding must be implemented immediately.
To ensure $p$ -values are not reported, since they are unrelated to research interpretation.
To treat clinical significance as equivalent to randomization and blinding procedures.
To avoid assuming statistically detectable differences translate into meaningful improvements for clients' daily functioning. (correct answer)

Question 14

In a CBT outcome study, mean symptom drop is statistically significant ( $p=9.0\times10^{-3}$ ) but below a preset clinical threshold; what differs?

Clinical significance concerns meaningful improvement; statistical significance concerns chance-based evidence. (correct answer)
Clinical significance is determined only by $N$ , while statistical significance is determined only by $d$ .
If results are statistically significant, they must meet any clinical threshold automatically.
Clinical significance is the probability of detecting an effect when it exists.

Explanation: This question tests understanding of statistical interpretation concepts such as effect size, power, and clinical vs. statistical significance in psychological research. Effect size measures the magnitude of an effect, power determines the likelihood of detecting an effect if it exists, and clinical significance assesses the practical importance of findings. In this CBT study with p=0.009 but symptom drop below clinical threshold, it shows statistical evidence without practical meaning. The correct answer differentiates that clinical significance concerns meaningful improvement, while statistical concerns chance-based evidence. A common distractor assumes statistical significance automatically meets clinical thresholds. To assist learners, emphasize thresholds in outcome studies. Encourage integrating both significances for treatment evaluation.

Question 15

In an anxiety-treatment RCT ( $N=240$ ), CBT shows $p=2.0\times10^{-2}$ , $d=0.25$ ; why separate clinical and statistical significance?

If $p<.05$ , the effect is automatically large enough to change clinical practice.
Clinical significance is the probability of detecting the null hypothesis when it is true.
Clinical significance evaluates meaningful symptom and functioning change beyond whether $p<.05$ . (correct answer)
Statistical significance determines whether outcomes generalize to all diagnoses without replication.

Explanation: This question tests understanding of statistical interpretation concepts such as effect size, power, and clinical vs. statistical significance in psychological research. Effect size measures the magnitude of an effect, power determines the likelihood of detecting an effect if it exists, and clinical significance assesses the practical importance of findings. In this RCT scenario with p=0.02 and d=0.25, the question highlights why statistical significance alone may not imply meaningful change, as the small effect size suggests limited practical impact despite the significant p-value. The correct answer explains that clinical significance evaluates meaningful symptom and functioning changes beyond just whether p<0.05, emphasizing real-world relevance. A common distractor assumes that p<0.05 automatically means the effect is large enough for clinical practice, which ignores effect size and practical implications. To assist learners, emphasize evaluating both statistical and clinical significance in treatment studies. Encourage critical review of research by considering effect sizes alongside p-values to better inform clinical decisions.

Question 16

A meta-analysis reports $d=0.30$ for exposure therapy; what is the primary role of effect size in psychological research?

It sets the acceptable Type I error rate for future trials.
It indicates whether each included study achieved $p<5.0\times10^{-2}$ .
It determines whether publication bias exists without additional information.
It summarizes how large the typical treatment impact is across studies, aiding practical interpretation. (correct answer)

Question 17

A small ADHD trial ( $N=18$ ) shows improvement but $p=2.0\times10^{-1}$ ; how does statistical power influence interpretation?

Power is the same as clinical significance, so symptom change is irrelevant.
Low power guarantees the null hypothesis is true, so treatment should be abandoned.
Power is unrelated to false negatives and only affects false positives.
Low power increases risk of missing real effects, so non-significance should be interpreted cautiously. (correct answer)

Question 18

A sleep intervention shows $d=0.35$ with $80\%$ power but $p=6.0\times10^{-2}$ ; how does power influence interpretation?

Power is the probability that the null hypothesis is true in this sample.
Power means the result is clinically significant even if the effect is small.
Power is solely the sample size, so reporting $d$ is unnecessary.
Adequate power suggests the study was reasonably able to detect the expected effect, so $p$ near .05 is informative. (correct answer)

Question 19

A trauma-treatment pilot ( $N=24$ ) reports $d=0.60$ but $p=1.2\times10^{-1}$ ; how does power affect interpretation?

Power is unrelated to hypothesis testing and only applies to descriptive studies.
Because $p>.05$ , the effect size must be interpreted as zero.
Power is the same as the confidence interval width, so no other information is needed.
Low power means the study may miss a real effect; effect size still informs potential impact. (correct answer)

Explanation: This question tests understanding of statistical interpretation concepts such as effect size, power, and clinical vs. statistical significance in psychological research. Effect size measures the magnitude of an effect, power determines the likelihood of detecting an effect if it exists, and clinical significance assesses the practical importance of findings. In this trauma pilot with N=24, d=0.60, but p=0.12, low power from small N may explain non-significance despite a medium effect. The correct answer notes that low power means the study may miss a real effect, but effect size still informs potential impact. A common distractor claims non-significance makes effect size zero, which is incorrect. To assist learners, highlight power's role in small-sample studies. Encourage considering effect sizes even in non-significant results.

Question 20

A workplace program shows $p=4.0\times10^{-2}$ and $d=0.05$ in $N=2{,}000$ ; which scenario best illustrates effect size and power importance?

If $d$ is small, power must be low, so the result is invalid.
Because $p<.05$ , the effect must be clinically meaningful, regardless of $d$ .
Effect size is unnecessary when $N$ is large because power replaces interpretation.
High power makes tiny effects statistically significant, so effect size is needed to judge practical importance. (correct answer)