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* **Nikmati Kontennya:** Akhir kata, selamat menikmati tayangan **RCTI**! Jangan lupa untuk selalu mengikuti perkembangan informasi terbaru seputar **RCTI** dan **Vision+**.
Alright, so we've sung the praises of **unbiased point estimators**. But what happens when things *aren't* unbiased? When does an estimator tend to systematically miss the mark? A classic case where bias creeps in is when we use a biased version of the sample variance, as mentioned before. If you compute the sample variance using division by $n$ instead of $n-1$, you're introducing a downward bias. This means your estimated variance will, on average, be smaller than the true population variance. Why does this happen? Intuitively, when you calculate the sample variance, you're using the *sample mean* ($\bar{X}$) instead of the true population mean ($\mu$) in the calculation. Since the sample mean is the value that minimizes the sum of squared deviations from the data points, using it instead of the true mean introduces a slight
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