Sampling Distribution Review
📌 A sampling distribution is created by repeatedly taking samples from a population, calculating a statistic (like the sample mean, $\bar{x}$), and then plotting these statistics to form a distribution.
📊 For a sampling distribution of the sample mean, each point on the final graph represents the mean ($\bar{x}$) calculated from one simple random sample.

Central Limit Theorem (CLT) Definition
⚙️ The Central Limit Theorem (CLT) predicts the shape of a sampling distribution based on the sample size ($n$).
🔄 The theorem states that if the sample size ($n$) is large enough, the sampling distribution of the sample mean will be approximately normal, regardless of the original population's distribution.
📉 Even if the original population distribution is skewed, the resulting sampling distribution of the sample mean tends toward a normal shape if $n$ is sufficient.

CLT Application and Sample Size Threshold
✅ The general rule of thumb is that the CLT can be safely applied when the sample size $n$ is greater than or equal to 30 ($n \geq 30$).
⚠️ For sample sizes $n < 30$, the normal approximation may not be accurate due to increased variability and risk of obtaining unusual samples by chance.
⭐ An exception is when the population distribution is already normally distributed; in this case, the sampling distribution will be normal even for smaller sample sizes.

Key Points & Insights
➡️ The CLT allows analysis of large datasets by assuming the sampling distribution of the mean is normal, enabling the use of normal distribution formulas for interpretation.
➡️ In practice, larger sample sizes are generally preferred for more precise estimates of the population distribution, even if the population is normal.
➡️ Options where $n < 30$ without an already normal population (A and B in the examples) do not guarantee an approximately normal sampling distribution.
➡️ Options where $n \geq 30$ (C, D, F) or where the population is inherently normal (E) will produce an approximately normal sampling distribution.

📸 Video summarized with SummaryTube.com on Dec 03, 2025, 13:13 UTC