Minds ExploredBy Evidence Press

Learning path

Read a quantitative paper critically

A ten-step reading sequence that moves from the recorded variables and descriptive patterns to the tested parameter, uncertainty, model assumptions and prediction claims. Use the linked causal and transparency readings for questions the statistical calculation cannot answer.

For: A reader who wants to evaluate a reported quantitative result without beginning by reproducing every equation.

What you will work towards

  • Identify the recorded variables, denominators and target quantity.
  • Distinguish effect size, p-value and interval uncertainty.
  • Recognise when a regression claim changes from association to prediction or causation.
  • Write a concise assessment with explicit unresolved information.
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  1. NPTEL — National Programme on Technology Enhanced Learning · Source-checked annotation

    Lecture 07 : Variables and Types of Data

    NPTEL — National Programme on Technology Enhanced Learning Speaker not established foundation Duration unknown

    Start with what the paper actually recorded, before reading its conclusion.

    Try this

    List each main variable, how it was obtained and the proposed interpretation.

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  2. NPTEL — National Programme on Technology Enhanced Learning · Source-checked annotation

    Lecture 08 : Absolute Frequency, Relative Frequency and Frequency Distribution

    NPTEL — National Programme on Technology Enhanced Learning Speaker not established foundation Duration unknown

    Check who or what contributes to each percentage.

    Try this

    Write the numerator, denominator and missing-data count for one reported proportion, or mark the missing information.

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  3. NPTEL — National Programme on Technology Enhanced Learning · Source-checked annotation

    Lecture 18 : Range, Interquartile Range and Quartile Deviation

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    Add spread to the reported centre.

    Try this

    Find a spread measure and state whether it describes individual variation or uncertainty in an estimate.

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  4. NPTEL — National Programme on Technology Enhanced Learning · Source-checked annotation

    Lecture 25 : Univariate and Bivariate Scatter Plots

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    Inspect the pattern before accepting a single association summary.

    Try this

    Sketch what the scatterplot would need to reveal, including unusual points or separate groups.

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  5. NPTEL — National Programme on Technology Enhanced Learning · Source-checked annotation

    Hypothesis testing of variance and proportions

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    Identify which kind of parameter the analysis tests.

    Try this

    Decide whether the reported test concerns a mean, a variance or a proportion; record its assumptions rather than guessing from the p-value.

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  6. NPTEL — National Programme on Technology Enhanced Learning · Source-checked annotation

    p value

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    Interpret the p-value as a conditional calculation.

    Try this

    Rewrite the p-value sentence without assigning a probability that the hypothesis is true.

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  7. NPTEL — National Programme on Technology Enhanced Learning · Source-checked annotation

    Hypothesis testing using confidence interval

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    Relate the test to the effect estimate and matching interval.

    Try this

    State which effect sizes remain compatible with the reported uncertainty under the chosen method.

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  8. NPTEL — National Programme on Technology Enhanced Learning · Source-checked annotation

    Statistic for linear regression

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    Inspect the statistical model behind a coefficient.

    Try this

    List the outcome, predictors and one error-model assumption; label a causal claim separately.

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  9. NPTEL — National Programme on Technology Enhanced Learning · Source-checked annotation

    Hypothesis testing in linear regression

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    Separate fitting the observed data from predicting new observations.

    Try this

    Identify whether an interval refers to a fitted mean or a new case, and what evidence of out-of-sample performance is available.

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  10. NPTEL — National Programme on Technology Enhanced Learning · Source-checked annotation

    Statistics for Hypothesis Testing - Part 1

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    Return to the sampling model that makes the inference meaningful.

    Try this

    Write a five-sentence appraisal: observation, estimate, uncertainty, assumption and unresolved reporting item. Consult TOP before claiming the result is reproducible.

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A short starting route

Steps 1, 2, 4, 6, 7. This gives a useful first reading of measurements, denominators, patterns and uncertainty before the regression material.

Continue more deeply

Read Chapters 1–3 of Causal Inference: What If when the paper’s conclusion concerns an intervention rather than an association. Continue at source ↗

Limits of this route

The video descriptions were checked against identified official-transcript excerpts, not complete viewing. An accessible official media endpoint is not evidence that every frame played. Teaching examples are not independent validation of the underlying scientific or clinical claims. The route supplies an initial appraisal, not an independent statistical reanalysis. An unavailable method, dataset or analysis plan remains an explicit gap.

Sources and editorial check · 2026-11-07

All step selections checked against their individually identified transcript excerpts; supporting reading sections recorded in source receipts.

Checked by agent. This is not human peer review.

  1. ASA statement on statistical significance and p-values — 2016 release

    Retrieved 2026-11-07. Read the three-page release, especially the six principles on page 2; used for interpretation and reporting cautions, not as a complete statistics textbook.

  2. NIST/SEMATECH e-Handbook — Confidence Limits for the Mean

    Retrieved 2026-11-07. Read the interval interpretation, formula, width factors and corresponding one-sample test, rendered lines 7–25; worked example inspected as illustration, not independently recomputed.

  3. Center for Open Science — TOP Guidelines

    Retrieved 2026-11-07. Read the TOP 2025 overview and tables of research practices, verification practices and study types, rendered lines 15–49. The page distinguishes disclosure, sharing and independent verification.

  4. Hernán and Robins — Causal Inference: What If, download labelled 19 August 2026

    Retrieved 2026-11-07. Inspected identified excerpts only: first 2,200 extracted characters of PDF pages 8, 12, 13, 14; the extracted text of pages 22, 24, 36, 37, 39, 41, 42 and 44. These cover causal questions, counterfactuals, randomisation, consistency, exchangeability and positivity. No whole-book reading claimed.

  5. Lecture 07 : Variables and Types of Data

    Retrieved 2026-11-07. Selected excerpts only: segment 0 (header) first 163 characters; segment 1 (00:19) first 555 characters; segment 13 (15:25) first 1400 characters; segment 24 (34:24) first 1352 characters. No complete transcript reading or full video viewing claimed.

  6. Lecture 08 : Absolute Frequency, Relative Frequency and Frequency Distribution

    Retrieved 2026-11-07. Selected excerpts only: characters 0–1400 in cached extraction; characters 1400–2800 in cached extraction; characters 16962–18362 in cached extraction; characters 32524–33924 in cached extraction. No complete transcript reading or full video viewing claimed.

  7. Lecture 18 : Range, Interquartile Range and Quartile Deviation

    Retrieved 2026-11-07. Selected excerpts only: segment 0 (header) first 1231 characters; segment 1 ((01:56) first 1400 characters; segment 14 ((31:12) first 419 characters; segment 26 ((43:17) first 1313 characters. No complete transcript reading or full video viewing claimed.

  8. Lecture 25 : Univariate and Bivariate Scatter Plots

    Retrieved 2026-11-07. Selected excerpts only: segment 0 (header) first 1118 characters; segment 1 (01:42) first 1400 characters; segment 12 (25:38) first 1069 characters; segment 23 (36:02) first 1400 characters. No complete transcript reading or full video viewing claimed.

  9. Hypothesis testing of variance and proportions

    Retrieved 2026-11-07. Selected excerpts only: segment 0 (header) first 205 characters; segment 1 (00:09) first 584 characters; segment 14 (29:06) first 1400 characters; segment 27 (67:52) first 1400 characters. No complete transcript reading or full video viewing claimed.

  10. p value

    Retrieved 2026-11-07. Selected excerpts only: segment 0 (header) first 166 characters; segment 1 (00:01) first 124 characters; segment 7 (15:43) first 1400 characters; segment 12 (26:04) first 1400 characters. No complete transcript reading or full video viewing claimed.

  11. Hypothesis testing using confidence interval

    Retrieved 2026-11-07. Selected excerpts only: segment 0 (header) first 202 characters; segment 1 (00:01) first 1207 characters; segment 6 (07:42) first 1400 characters; segment 10 (16:30) first 1090 characters. No complete transcript reading or full video viewing claimed.

  12. Statistic for linear regression

    Retrieved 2026-11-07. Selected excerpts only: segment 0 (header) first 435 characters; segment 1 (00:23) first 1400 characters; segment 5 (26:32) first 1400 characters; segment 8 (35:04) first 1400 characters. No complete transcript reading or full video viewing claimed.

  13. Hypothesis testing in linear regression

    Retrieved 2026-11-07. Selected excerpts only: segment 0 (header) first 198 characters; segment 1 (00:09) first 0 characters; segment 8 (13:37) first 1400 characters; segment 14 (34:51) first 1400 characters. No complete transcript reading or full video viewing claimed.

  14. Statistics for Hypothesis Testing - Part 1

    Retrieved 2026-11-07. Selected excerpts only: segment 0 (header) first 201 characters; segment 1 (00:09) first 567 characters; segment 13 (33:48) first 1400 characters; segment 24 (64:46) first 1400 characters. No complete transcript reading or full video viewing claimed.

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