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Sample standard deviation can underestimate or overestimate the true population standard deviation, introducing bias into point estimates. This occurs because a single sample may not perfectly represent the entire population. To address this limitation, confidence intervals are constructed around the sample statistic to provide a range of reasonable values where the population parameter likely falls.

Only randomly selected samples from normally distributed populations can be used to estimate population parameters reliably. Random sampling ensures that the sample is representative of the population, while the normality assumption validates the use of specific statistical distributions for constructing confidence intervals and determining critical values.

First, use a chi-square table to find the right and left critical values corresponding to your desired confidence level. Then calculate confidence intervals for population variance using these critical values separately. Finally, take the square root of the variance intervals to obtain the confidence intervals for population standard deviation, which can be rounded for practical use.

A 95% confidence interval provides a range of values where you can be 95% confident the true population standard deviation lies. For example, if the interval is 1.03 to 2.74 degrees Celsius, there is a 95% probability that the actual population standard deviation falls within this range. However, there is no guarantee any single interval captures the true parameter.

Critical values from the right and left tails of the chi-square distribution curve define the boundaries of the confidence interval for population standard deviation. These values correspond to your chosen significance level and sample size. They determine how wide or narrow your confidence interval will be around the sample estimate.

The sample standard deviation can reasonably estimate the population standard deviation only when sample size n is sufficiently large. Larger samples provide more stable and reliable estimates because they better represent population variability. Small samples are more likely to produce biased estimates that deviate significantly from the true population parameter.

Confidence intervals overcome the bias inherent in point estimates by providing a range rather than a single value. This range accounts for sampling variability and uncertainty, offering a more realistic representation of where the true population parameter likely exists. The interval width reflects both the sample variability and the chosen confidence level.