📏 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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