Read the Evidence. Not Just the Headline.
Read study results in practical terms: what changed, how large the difference was, how precisely it was estimated, and who was studied.
One Result. Five Different Questions.
The cleanest way to read exercise-science results is to separate the questions instead of asking one statistic to answer all of them.
What physically changed?
Pounds, kilograms, repetitions, meters, centimeters, m/s, watts, or %MVC. Start here because this is the result an athlete can recognize.
How uncertain is the estimate?
Confidence intervals and variability show how tightly or loosely the study estimated the effect.
How compatible are the data with no difference?
The p-value is an evidence signal under a statistical model. It is not the size of the result.
How large was the standardized separation?
Cohen’s d and Hedges’ g express a difference relative to the variability used in the calculation.
What conclusion can the study support?
An acute crossover, a randomized training trial, and a meta-analysis answer different questions.
That sequence is more useful than stopping at “statistically significant.”
Could the Observed Difference Reflect Random Variation?
Under the study’s null model and assumptions, p < .001 means that data at least this extreme would be expected less than 0.1% of the time if there were no underlying difference of the kind tested. That is strong incompatibility with the null model, not a 99.9% probability that an intervention “works.”1
How surprising are the data under “no difference”?
Smaller p-values generally indicate that the observed data are less compatible with the no-difference model, assuming the analysis and study conditions are appropriate.
Read the raw change and effect size together.
Kilos, reps, or meters show practical change; effect size shows it relative to variability.
Read the confidence interval with the estimate.
A narrower interval generally indicates greater precision. A wider interval means a broader range of values remains compatible with the data and model.
How Large Is the Difference?
Both express a difference in standard-deviation units. The exact calculation must match the study design, and Hedges’ g adds a correction intended to reduce small-sample bias.23
A difference expressed relative to variability.
At its simplest, Cohen’s d divides a difference between averages by a standard deviation. A d of 1.0 means the difference is about one standard deviation, not 100% improvement.
- The formula changes for independent groups, paired observations, and crossover designs.
- The published acute crossover paper reports Cohen’s d values from 0.23 to 1.02.
The same basic idea with a small-sample correction.
Hedges’ g applies a correction to reduce the tendency of standardized mean differences to be biased upward in smaller samples. Hedges’ foundational work demonstrated that small-sample bias and derived an adjusted estimator.3
- In larger samples, d and g are usually very similar.
- In smaller samples, g is generally slightly smaller than the uncorrected d.
- The published longitudinal trials report Hedges’ g values from 3.67 to 4.10.
Why a Small p-Value Does Not Tell You the Size of a Benefit
Cohen’s familiar 0.2, 0.5, and 0.8 guideposts are useful orientation points, but they are conventions, not universal biological laws. The outcome, population, design, and effect-size formula all influence interpretation.24
Cohen’s d = 0.23–1.02
Five outcomes showed significant moderate-to-large effects; concentric power was small and non-significant (d = 0.23, p = .071).
Read the acute study →Hedges’ g = 3.85
The Launch Pad group gained 18.4 kg on average versus 11.1 kg for the Standard Flat Bench group, a 7.3 kg / 16.1 lb between-group advantage under the matched protocol.
Read the four-week study →Hedges’ g = 3.99, 3.67, and 4.10
The published paper reported exceptionally large standardized between-group effects across 1-RM strength, NFL-225 repetitions, and seated medicine-ball throw distance.
Read the eight-week study →How Unusual Is an Effect Size Near 4.0?
The longitudinal effect sizes are exceptionally large against conventional benchmarks and the exercise-science studies below.
Exceptionally large by conventional benchmarks and selected exercise-science comparisons.
A 2024 meta-analysis covering 295 strength-and-conditioning studies, 535 groups, and 6,710 participants reported a pooled intervention-only standardized mean difference of 0.55. After outlier screening, retained effects ranged from −0.83 to 4.7. Values near 4 therefore sit close to the extreme upper end of that broad reported range, not near the pooled center.6
The 2024 database reports intervention-only standardized changes; the Launch Pad trials report between-group Hedges’ g. Compare these estimates in the context of their populations, outcomes, and calculations.
| Source | Evidence set | Reported standardized effect | Why it matters here |
|---|---|---|---|
| Swinton et al., 2024 | 295 resistance-only or resistance-dominant studies; 535 groups; 6,710 participants. | Pooled intervention-only SMD = 0.55; retained range −0.83 to 4.7 after outlier screening. | Provides a large strength-and-conditioning database. Values near 4 are close to the upper extreme of the retained range. |
| Hagstrom et al., 2020 | 24 studies of resistance-training adaptations in women. | Upper-body strength g = 1.70; lower-body strength g = 1.40. Trim-and-fill estimates: 1.07 and 0.52. | Shows that pooled training effects can exceed 0.8 while remaining materially below the Launch Pad longitudinal values. |
| Martínez-García et al., 2021 | 16 controlled resistance-training studies; 424 overhead athletes. | Throwing-velocity ES = 1.10; 95% CI 0.64–1.57. | Offers a sport-performance transfer comparison based on controlled trials. |
| Makaruk et al., 2024 | 35 studies; 777 elite athletes. | Sport-specific performance SMD = 1.16; 95% CI 0.65–1.66. | Provides a pooled effect from elite-athlete resistance-training literature. |
| Hammert et al., 2026 | High-load versus low-load isotonic training for non-specific strength. | Cohen’s d = 0.322; 95% CI −0.08 to 0.72. | Illustrates that pooled differences between two active resistance-training approaches are often modest and uncertain. |
“Large” depends on the outcome.
In 114 exercise-therapy studies for tendinopathy, context-specific modeled quartiles across all outcomes were approximately 0.34, 0.73, and 1.21. Thresholds varied substantially by outcome domain, reinforcing that effect sizes should be interpreted against relevant evidence rather than one universal scale.5
How Uncertain Is the Estimate?
A large standardized effect can reflect a large group difference, low variability, a tightly controlled protocol, or a combination. Check the raw change, sample, and confidence interval together.
Keep the raw difference visible.
Effect size should never replace pounds, repetitions, meters, or the actual group means.
Inspect variability and confidence.
Large separation with narrow uncertainty is more stable than a large point estimate surrounded by wide uncertainty.
Read the sample and protocol.
Small, homogeneous, supervised studies may estimate a strong effect under conditions that do not transfer perfectly elsewhere.
Look for convergence and replication.
Three studies asking different questions are stronger than one isolated result, but independent replication remains valuable.
What Can This Study Design Establish?
What changes during the rep?
Compares immediate responses to two conditions within the same lifter.
What accumulates over weeks?
Compares changes over a defined training program between randomly allocated groups.
What pattern appears across studies?
Combines findings across studies, with interpretation shaped by their quality, comparability, and methods.
Read a Study in Ten Steps.
Who was studied?
Sex, age, training status, sport, health status, and sample size.
What was compared?
The exact intervention, comparator, equipment, and training conditions.
What was randomized?
Check whether participants, order, or condition were randomized and how allocation was handled.
What stayed the same?
Program, load, volume, coaching, testing, recovery instructions, and measurement methods.
What changed in raw units?
Kilograms, pounds, repetitions, meters, m/s, watts, %MVC, or centimeters.
How precise was the estimate?
Read confidence intervals, standard deviations, and sample size, not only the point estimate.
What did the p-value say?
The compatibility of the data with a no-difference model, not effect magnitude.
What did the effect size say?
The standardized magnitude calculated for that specific design. It is not a multiplier.
How was the outcome measured?
Check the test, measurement method, and timing.
Does it fit my decision?
Transfer the finding only as far as the population, protocol, and outcome justify.
Keep the Literature Attached to the Interpretation.
The statistical explanations and effect-size context on this page are grounded in methodological literature, exercise-science syntheses, and the three published Launch Pad studies.
- Wasserstein, R. L., & Lazar, N. A. (2016). The ASA’s statement on p-values: Context, process, and purpose. The American Statistician, 70(2), 129–133. Open DOI
- Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, 863. Open DOI
- Hedges, L. V. (1981). Distribution theory for Glass’s estimator of effect size and related estimators. Journal of Educational Statistics, 6(2), 107–128. Open DOI
- Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.
- Swinton, P. A., et al. (2023). What are small, medium and large effect sizes for exercise treatments of tendinopathy? A systematic review and meta-analysis. BMJ Open Sport & Exercise Medicine, 9(1), e001389. Open DOI
- Swinton, P. A., Schoenfeld, B. J., & Murphy, A. (2024). Dose–response modelling of resistance exercise across outcome domains in strength and conditioning: A meta-analysis. Sports Medicine, 54, 1579–1594. Open DOI
- Hagstrom, A. D., Marshall, P. W., Halaki, M., & Hackett, D. A. (2020). The effect of resistance training in women on dynamic strength and muscular hypertrophy: A systematic review with meta-analysis. Sports Medicine, 50(6), 1075–1093. Open DOI
- Martínez-García, D., et al. (2021). Strength training for throwing velocity enhancement in overhead throw: A systematic review and meta-analysis. International Journal of Sports Science & Coaching, 16(5). Open DOI
- Makaruk, H., Starzak, M., Tarkowski, P., Sadowski, J., & Winchester, J. (2024). The effects of resistance training on sport-specific performance of elite athletes: A systematic review with meta-analysis. Journal of Human Kinetics, 91, 135–155. Open DOI
- Hammert, W. B., et al. (2026). Non-specific strength changes between high- and low-load isotonic resistance training: A systematic review and meta-analysis. Sports Medicine, 56, 763–773. Open DOI
- Kidwell, J. A., et al. (2026). Acute effects of thoracic-spinal elevation via a novel bench press pad on sEMG and barbell kinetics in resistance-trained males. International Journal of Exercise Science, 19(1), 1003. Open DOI
- Goldman, P., et al. (2025). Eccentrically overloaded bench press training: Augmenting strength gains via a novel bench press pad. Scientific Journal of Sport and Performance, 4(4), 480–490. Open DOI
- Blatney, A. E., et al. (2026). Effects of an eight week training regimen with a novel bench press pad compared to a traditional bench on upper body strength and performance in collegiate American football players. Scientific Journal of Sport and Performance, 5(1), 10–21. Open DOI