Distinguishing Sample vs. Sampling Distributions
📌 A sample distribution interprets data from a singular sample taken from a population.
📊 A sampling distribution is the distribution of a statistic (like the sample mean, $\bar{x}$) derived from multiple simple random samples drawn from a specific population.
📉 Sample means ($\bar{x}$) will vary sample-to-sample and may not equal the population mean ($\mu$).

Characteristics of Population vs. Sampling Distributions
⭐ The population distribution has mean $\mu$ and standard deviation $\sigma$.
📏 The mean of the sampling distribution of the sample mean ($\mu_{\bar{x}}$) equals the population mean ($\mu$).
📈 The standard deviation of the sampling distribution, called the standard error ($\sigma_{\bar{x}}$), is $\frac{\sigma}{\sqrt{n}}$, making it smaller than the population standard deviation ($\sigma$).

Standardization and Application
⚙️ The standardization formula for a population distribution is $Z = \frac{X - \mu}{\sigma}$.
📐 The standardization formula for a sampling distribution of the sample mean is $Z = \frac{\bar{x} - \mu}{\sigma / \sqrt{n}}$.
💡 Sampling distributions are useful for estimating $\mu$ without measuring the entire population and calculating the probability of specific sample outcomes based on sample size ($n$).

Example Calculation (Sampling Distribution)
🇨🇦 For Canadian heights ($\mu = 160$ cm, $\sigma = 7$ cm), the standard error for $n=10$ is $\frac{7}{\sqrt{10}} \approx 2.21$.
🔢 The probability that the average height of 10 Canadians is less than 157 cm corresponds to a Z-score of $\frac{157 - 160}{2.21} \approx -1.36$, yielding a probability of 0.0869.

Example Calculation (Population Distribution)
🧍 To find the proportion of all people with heights greater than 170 cm (population distribution), the Z-score is $Z = \frac{170 - 160}{7} \approx 1.43$.
🔢 Since the Z-table gives the area to the left (0.9236), the area to the right ($P(X > 170)$) is $1 - 0.9236 = 0.0764.

Key Points & Insights
➡️ A sampling distribution is essential because it offers convenience and efficiency in estimating population parameters ($\mu$) without measuring every individual.
➡️ The Standard Error ($\frac{\sigma}{\sqrt{n}}$) quantifies that averages (used in sampling distributions) exhibit less variability than individual observations (in population distributions).
➡️ To solve probability questions involving sample averages ($\bar{x}$), always use the sampling distribution formulas which incorporate the sample size $n$ into the standard deviation calculation.

📸 Video summarized with SummaryTube.com on Dec 02, 2025, 13:05 UTC