Practice statistical language for research: 'statistically significant', p-value communication, 'we observe that', 'correlation does not imply causation', confidence intervals, and effect sizes.
0 / 10 completed
1 / 10
'The improvement was _____ significant (p < 0.05).' What does this phrase mean in a research paper?
'Statistically significant' means the observed result is unlikely to be due to random chance, given the chosen significance threshold (alpha). p < 0.05 means there is less than a 5% probability of seeing this result if the null hypothesis were true. Important: statistical significance does not mean practical significance — a tiny, unimportant difference can be statistically significant with a large enough sample.
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'We report a p-value of 0.003.' How should this be interpreted?
A p-value is the probability of observing data at least as extreme as the sample results, assuming the null hypothesis is true. p = 0.003 means this outcome would occur only 0.3% of the time by chance under H0 — strong evidence against the null. Common misreading: the p-value is NOT the probability that H0 is true, and NOT the probability the result will replicate.
3 / 10
'We _____ that latency increases sharply above 1,000 concurrent connections.' Which verb is appropriate for reporting an empirical observation in a results section?
'We observe that' is the standard, epistemically careful phrase for reporting what was measured in an experiment. It does not claim causality or generalisability beyond the observed data. 'We prove' is too strong (proof belongs to mathematics). 'We discover' implies a novel finding not everyone uses. 'We observe' is neutral and precise.
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'Higher ice cream sales correlate with higher drowning rates. Therefore, ice cream causes drowning.' What fundamental principle of statistical reasoning does this violate?
'Correlation does not imply causation' is one of the most important phrases in research English. Both ice cream sales and drowning increase in hot weather — the confound is temperature. In research papers: 'We observe a positive correlation between X and Y (r = 0.72), though we caution that this does not establish a causal relationship.' To establish causation you need a controlled experiment, not just correlation.
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'The 95% _____ interval for the mean response time is [142 ms, 158 ms].' What does this interval communicate?
A 95% confidence interval means: if we repeated this study many times, 95% of the computed intervals would contain the true population parameter. It expresses estimation uncertainty. Narrower CI = more precise estimate (larger sample). Wider CI = more uncertainty (smaller sample). Correct language: 'the 95% CI [142, 158]' — not 'there is a 95% probability the true mean is in this interval' (a Bayesian interpretation).
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Reviewer: 'The statistical analysis revealed a statistically significant difference (p = 0.01) between the control group and the treatment group regarding user engagement.' What is the primary implication of this finding for your code's performance metrics?
The p-value (0.01) shows a statistically significant difference, but it doesn't tell you *how* large or important that difference is in the real world. Option 2 correctly emphasizes that significance needs to be considered alongside practical impact and potential confounding factors. Options A and B misinterpret the meaning of p-values, while option D ignores the core finding entirely.
7 / 10
Team Lead (Sarah): 'I'm seeing a lot of discussion about confidence intervals. We've calculated a 95% CI for the new feature's conversion rate: [0.12, 0.18]. Can someone explain what this means in terms of our risk assessment?
The correct response accurately explains that a 95% confidence interval provides a range within which the true conversion rate is likely to fall, with 95% probability. Options A and C misunderstand the purpose of a CI; option B incorrectly frames it as a measure of certainty, and option D focuses on the width of the interval rather than its interpretation as a range.
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Developer (David): 'Implemented A/B testing. Results: Group A - Conversion Rate: 5.2%, Standard Deviation: 1.1%. Group B - Conversion Rate: 6.8%, Standard Deviation: 0.9%. Paired t-test, p < 0.01.' Which of the following phrases best summarizes this data for a non-technical stakeholder?
This option distills the key findings—significant improvement and stable performance—into understandable terms for someone without statistical expertise. The p-value is mentioned to reinforce the significance, while options A focuses on technical details, C misinterprets the standard deviation's role, and D highlights a lack of information.
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Engineer (Maria): 'We ran a cohort analysis on user retention. We found a statistically significant drop-off after the first week for users who didn't complete onboarding – p = 0.03.' When explaining this to the product team, what is the MOST important point to emphasize?
Maria's statement highlights a critical problem – a statistically significant drop-off after onboarding. Emphasizing this directly prompts action for the product team. Option A is too narrow; option B focuses on a potential issue without context; option C downplays the importance of the finding, and option D introduces extraneous considerations.
10 / 10
API Response (JSON): `{"statistic": "user_retention", "period": "7_days", "p_value": 0.03, "confidence_interval": [0.15, 0.22], "data_source": "new_users"}` Reviewer: This API response indicates a statistically significant change in user retention. What does the confidence interval [0.15, 0.22] primarily communicate?
The confidence interval provides a range of values within which we can be reasonably sure the true user retention rate lies. It's a probabilistic statement – 95% of all possible samples would have yielded an interval containing the true value. Options A and B misinterpret the CI's function, while option D is factually incorrect.
What will I practice in "Statistical Language in Research Papers"?
This is a Research English exercise set. It walks through 10 scenario-based multiple-choice questions built around real usage of research english terminology that IT professionals encounter on the job.
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How many questions are in this exercise?
This set contains 10 questions. Each one shows immediate feedback and a detailed explanation after you answer, so you learn the correct usage right away rather than waiting for a final score.
Do I need prior experience to complete this exercise?
No prior experience is required. Each question includes a full explanation covering the reasoning behind the correct answer, so the exercise itself teaches the research english vocabulary as you go.
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What if I don't understand a term used in a question?
Read the explanation shown after you answer each question — it breaks down the correct term in plain English with a real-world example. You can also check the site Glossary for quick definitions.
How is this different from reading a blog article on the topic?
Exercises like this one are interactive drills that test and reinforce specific vocabulary through multiple-choice questions, while blog articles explain concepts in prose. Practising here after reading builds active recall, not just passive recognition.
Where can I find more Research English exercises?
See the Research English exercises hub for the full set of related pages, or browse all exercise categories from the main Exercises index.
Can I use this exercise to prepare for a technical interview?
Yes — research english vocabulary comes up often in technical discussions and interviews. Pair this exercise with our dedicated Interview Preparation section for role-specific practice.