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develarist
develarist
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No matter the shape of the population distribution, the Central Limit Theorem holds that the sampling distribution of the sample means (and sample variances) approaches a normal distribution as the sample size $T$ (number of observations or data points) gets larger, especially for sample sizes over 30. As you take more samples, the sample distribution graph will look more like a normal distribution, and so on average, the sample moments will be the population moments as $T\uparrow$. (If you add up the means or standard deviations from all of your samples and find the average, that average will be close to the actual moments for the population.)

No matter the shape of the population distribution, the Central Limit Theorem holds that the sampling distribution of the sample means (and sample variances) approaches a normal distribution as the sample size (number of observations or data points) gets larger, especially for sample sizes over 30. As you take more samples, the sample distribution graph will look more like a normal distribution, and so on average, the sample moments will be the population moments as $T\uparrow$. (If you add up the means or standard deviations from all of your samples and find the average, that average will be close to the actual moments for the population.)

No matter the shape of the population distribution, the Central Limit Theorem holds that the sampling distribution of the sample means (and sample variances) approaches a normal distribution as the sample size $T$ (number of observations or data points) gets larger, especially for sample sizes over 30. As you take more samples, the sample distribution graph will look more like a normal distribution, and so on average, the sample moments will be the population moments as $T\uparrow$. (If you add up the means or standard deviations from all of your samples and find the average, that average will be close to the actual moments for the population.)

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develarist
develarist
  • 4.3k
  • 1
  • 26
  • 65

No matter the shape of the population distribution, the Central Limit Theorem holds that the sampling distribution of the sample means (and sample variances) approaches a normal distribution as the sample size (number of observations or data points) gets larger, especially for sample sizes over 30. As you take more samples, the sample distribution graph will look more like a normal distribution, and so on average, the sample moments will be the population moments as $T\uparrow$. (If you add up the means or standard deviations from all of your samples and find the average, that average will be close to the actual moments for the population.)