Mileage tests are conducted for a particular model of automobile. If a \(98 \%\) confidence interval with a margin of error of 1 mile per gallon is desired, how many automobiles should be used in the test? Assume that preliminary mileage tests indicate the standard deviation is 2.6 miles per gallon.

Understanding the margin of error is crucial when interpreting the results of a study or survey. It represents the extent to which the results from the sample can differ from the true population values. The margin of error is influenced by factors such as sample size, standard deviation, and confidence level. In the context of the exercise, a margin of error of 1 mile per gallon means that the true average mileage could be within 1 mile per gallon above or below the sample average.

To visualize this, consider a sample mean mileage of 30 miles per gallon. With a margin of error of 1 mile per gallon, the true population mean could lie between 29 and 31 miles per gallon. This margin helps researchers grasp how closely the sample represents the population, thereby guiding the degree of confidence in making claims based on the data.

Standard deviation is a measure that reflects the amount of variability or dispersion in a set of data. In simpler terms, it gives us an idea of how spread out the individual measurements are in relation to the mean (average). A low standard deviation indicates that the data points tend to be close to the mean, whereas a high standard deviation suggests a wider spread of values.

For example, in our mileage test, a standard deviation of 2.6 miles per gallon means that the mileage per gallon of most automobiles will be within 2.6 miles of the mean mileage. Understanding the standard deviation helps in assessing the reliability and precision of the sample results and is essential for calculating the sample size needed for a desired margin of error.

In statistics, the z-score reflects how many standard deviations an element is from the mean. It is a way of standardizing scores on the same scale to make them comparable. In the context of confidence intervals, the z-score corresponds to the desired confidence level. The higher the z-score, the more confident we are that the population parameter lies within the interval given by the margin of error.

For a 98% confidence level, as in our exercise, we use a z-score that captures the middle 98% of the data. If our z-score is 2.576, this means that the confidence interval extends 2.576 standard deviations on either side of the sample mean, ensuring that there is only a 2% chance that the true population parameter falls outside of this range.

Determining the right sample size is a balancing act: too small a sample may not adequately represent the population, while too large a sample could unnecessarily consume resources. The sample size is influenced by the margin of error, the standard deviation of the population, and the desired confidence level.

The formula \( n = (\frac{Z_{\frac{\alpha}{2}}\cdot \sigma}{E})^2 \) is used to find the necessary sample size () to achieve a given margin of error (\(E\) with a certain confidence level, represented by the z-score (\(Z_{\frac{\alpha}{2}}\) and the population's standard deviation (\(\sigma\) The higher the z-score or standard deviation, or the lower the margin of error desired, the larger the required sample size. Having a proper sample size is crucial for obtaining statistically significant results.

Statistical significance plays a pivotal role in hypothesis testing, where it helps researchers determine if their findings are likely due to chance. It is often denoted by the p-value, which is the probability of observing results at least as extreme as the ones obtained, given that the null hypothesis is true. A p-value that is less than the significance level (commonly 0.05) indicates that the results are statistically significant and that the null hypothesis can be rejected.

In relation to confidence intervals, statistical significance is tied to the confidence level: the chosen level (e.g., 98%) reflects the degree of confidence that the interval contains the true population parameter. A 98% confidence level means that if we were to take many samples and build a confidence interval from each, we would expect 98% of those intervals to contain the population mean, signifying a statistically significant result.