📏 The size of the effect is an important factor in statistical power.

🔎 Statistical analysis aims to provide both accurate and precise estimates.

🔢 Interval estimation is used to determine the range of possible values for the impact.

📏 The precision and confidence of statistical power are influenced by the size of the effect.

🎯 The goal is to have precise estimations with accurate numbers and a narrow range of variability.

🔍 Using the analogy of photographic images, larger effects are easier to identify with precision.

🔍 Identifying a larger image is easier and provides a clearer evaluation of impact.

🖼️ When comparing images of the same size, it is difficult to distinguish between them.

🧩 The closer image in the previous example is the true representation.

🔍 The size of the effect plays a role in statistical power.

📏 The sample size is like the zoom of an image.

💰 In microfinancing, a program with a larger effect size requires a smaller sample size.

🔍 A larger effect size requires a larger sample size or more zoom to identify the effect accurately.

⚖️ The precision needed for a given level of accuracy depends on the expected effect size.

📏 The sample size needed is influenced by the evaluator's prior knowledge and experience.

📊 A bigger sample size is needed to detect larger effects in statistical power.

🔍 Smaller effects require a larger sample size for accurate detection.

⚖️ The rule of thumb is that the size of the effect determines the required sample size.

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