The article by Daniël Lakens, titled "Sample Size Justification," discusses the importance of justifying the sample size in empirical studies to ensure that the collected data provides valuable information. The key aim is to explain how the data will inform the researcher's inferential goals. Six approaches to justify the sample size are discussed: 1) collecting data from almost the entire population, 2) choosing a sample size based on resource constraints, 3) performing an a-priori power analysis, 4) planning for a desired accuracy, 5) using heuristics, and 6) explicitly acknowledging the absence of a justification. The article emphasizes the importance of considering which effect sizes are deemed interesting and how these affect the informativeness of the data. Researchers should consider the smallest effect size of interest, the minimal statistically detectable effect, and the expected effect sizes. The article also highlights the value of information, the trade-off between data collection costs and the utility of the data, and the need to justify the sample size based on clear inferential goals. Additionally, it provides guidelines and an interactive form to help researchers improve their sample size justification and align the informational value of their studies with their inferential goals.The article by Daniël Lakens, titled "Sample Size Justification," discusses the importance of justifying the sample size in empirical studies to ensure that the collected data provides valuable information. The key aim is to explain how the data will inform the researcher's inferential goals. Six approaches to justify the sample size are discussed: 1) collecting data from almost the entire population, 2) choosing a sample size based on resource constraints, 3) performing an a-priori power analysis, 4) planning for a desired accuracy, 5) using heuristics, and 6) explicitly acknowledging the absence of a justification. The article emphasizes the importance of considering which effect sizes are deemed interesting and how these affect the informativeness of the data. Researchers should consider the smallest effect size of interest, the minimal statistically detectable effect, and the expected effect sizes. The article also highlights the value of information, the trade-off between data collection costs and the utility of the data, and the need to justify the sample size based on clear inferential goals. Additionally, it provides guidelines and an interactive form to help researchers improve their sample size justification and align the informational value of their studies with their inferential goals.