Approximately how many participants per sample are required to reach a statistical power of 0.80 for an independent-samples t-test with a large population effect?

True or False: In an independent-samples t-test with a large population effect, a researcher who wants to increase their statistical power from 0.80 to 0.99 would need to more than double their sample size per group (increasing from 26 to 59 participants).

A research lab is planning several studies using independent-samples t-tests and expecting a large population effect size in each case. Match each researcher's specific recruitment goal with the correct number of participants required for each of their sample groups.

A researcher is planning an independent-samples $t$-test and expects a large population effect size. Arrange the following recruitment requirements in order from the lowest number of participants to the highest number of participants required per sample.

You are formulating a recruitment strategy for a study using an independent-samples $t$-test where a large population effect is expected. Your goal is to design two protocol options: Protocol A for $0.80$ power and Protocol B for $0.99$ power. Which of the following recruitment summaries correctly assembles the total number of participants you must recruit across both group samples for each protocol?

A psychological researcher is planning an independent-samples $t$-test and expects a large effect size in the population. Match each of the researcher's goals or general observations with the appropriate sample size requirement according to statistical power standards.

In an independent-samples $t$-test with a large population effect, a researcher requires a smaller sample size (approximately 26 participants per sample) to achieve a statistical power of 0.99 than they do to achieve a power of 0.80 (approximately 59 participants per sample).

A researcher is planning an independent-samples $t$-test and expects a large population effect. Arrange the following group sample sizes in order of the statistical power they would provide, from the lowest power (top) to the highest power (bottom).

When conducting an independent-samples t-test and expecting a large population effect, approximately how many participants per sample are required to achieve a statistical power of 0.80?

A researcher conducting an independent-samples $t$-test with a large population effect size evaluates their study design and determines that a power level of $0.80$ (requiring $26$ participants per group) provides too high a risk of a Type II error. To achieve a more rigorous 'near-certainty' power level of $0.99$, the researcher must increase their recruitment to _____ participants per sample group.

Imagine you are evaluating the methodological rigor of a study using an independent-samples $t$-test with a large population effect. If the study currently uses $26$ participants per group to achieve a power of $0.80$, but your evaluation criteria require a much higher power level of $0.99$, you should recommend increasing the sample size to _____ participants per group.

Explain the relationship between sample size requirements and target statistical power for an independent-samples $t$-test when assuming a large population effect. In your explanation, detail how the required sample size per group changes as the researcher seeks to increase power from $0.80$ to $0.99$, and explain why sample sizes in psychological research must generally be larger than researchers might intuitively expect to achieve high power.

Apply the sample size benchmarks for an independent-samples $t$-test with a large population effect to Dr. Aris's study. Determine how many participants she must recruit per group to achieve a power of $0.80$, how many she would need to achieve a power of $0.99$, and justify whether she should adjust her initial plan of $20$ participants per group.

Analyze how the required sample size per group changes when a researcher decides to increase the statistical power of an independent-samples $t$-test (assuming a large population effect) from $0.80$ to $0.99$. What does this comparison reveal about the relationship between sample size increases and statistical power gains?