Hi everyone,
I used Cochran’s formula to get the sample size estimation, which is 140, and reviewer asked me what Is The lowest sample size to achieve the study objectives?
Could you please guide me what should I do?
Thanks,
Cochran’s formula helps to establish the minimum sample size. However, it does not mean that you must stick to the minimum sample size in your work. Good luck
It depends on which statistical test (e.g., t test, ANOVA, etc.) you are planning to use as well as on other factors such as the expected effect size, your desired level of statistical power (1 - beta), and tolerated Type-I (alpha) error probability. You can figure this out with a free power analysis program such as G*Power (https://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower).
When a reviewer asks about the lowest sample size to achieve study objectives, they are typically prompting you to ensure that your sample size is not just adequate but also the most efficient for detecting the effects you are interested in. This inquiry often leads to discussions about power analysis and the precision required for your specific study's parameters.
**Here's what you can do:**
1. **Review the assumptions made in your initial sample size calculation:** This includes the effect size you expect, the level of precision you desire (margin of error), the power of the study (commonly set at 0.80), and the significance level (commonly set at 0.05). Re-evaluating these can sometimes lead to a different sample size recommendation.
2. **Perform a detailed power analysis:** This involves more specifically defining the smallest effect size of interest (SESOI) and determining the minimum sample size required to reliably detect that effect with adequate power. Software like G*Power or statistical packages in R or Python can perform these calculations.
3. **Discuss the trade-offs:** In your response to the reviewer, it’s crucial to discuss the trade-offs involved in reducing the sample size. Lower sample sizes can increase the risk of Type II errors (failing to detect a true effect) and may reduce the generalizability of your results.
4. **Consider practical limitations:** Sometimes, the lowest theoretical sample size may not be practical due to anticipated dropout rates, the heterogeneity of the sample, or logistical constraints. Addressing how these factors influenced your sample size calculation can be very useful in your response.
5. **Literature and precedent:** Referencing similar studies or accepted practices in your field regarding sample size can also strengthen your argument. Showing that your sample size aligns with or exceeds typical standards can be persuasive.
6. **Recalculate if necessary:** If the reviewer’s question leads you to believe a recalibration of your sample size might be warranted, it might be worth doing so using updated assumptions based on any new information or feedback from the review.
When responding to the reviewer, it would be helpful to include the details of how you initially used Cochran’s formula and what assumptions were used (e.g., expected prevalence, margin of error, desired confidence level). Then, clarify how you addressed their concern about the lowest sample size by either justifying your existing calculation or presenting a new analysis based on adjusted assumptions. This response will demonstrate your thoroughness and the robustness of your research design.
It depends on the population size and the confidence interval. Use the following website to calculate the same.
http://www.raosoft.com/samplesize.html
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