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Confidence Intervals Objectives: Students should know how to calculate a standard error, given a sample mean, standard deviation, and sample size Students should know what a confidence interval is, and its purpose Students should be able to construct and interpret 90%, 95%, and 99% confidence intervals, and know how they compare to each other Students should know how changes in variability and/or changes in sample size affect the width of a confidence interval

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Confidence Intervals How precisely does the sample statistic estimate the population parameter ? To illustrate the calculation and interpretation of confidence intervals we’ll use the HR data from our previous sample of 84 adults: The sample mean HR was 74.0 bpm The sample standard deviation was 7.5 bpm

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Calculating Confidence Intervals What are the 90, 95, and 99% confidence intervals for our sample estimate of the true population heart rate? 90% CI: 74.0 ± 1.645(0.8) = 74.0 ± 1.3 = 72.7, % CI: 74.0 ± 1.960(0.8) = 74.0 ± 1.6 = 72.4, % CI: 74.0 ± 2.575(0.8) = 74.0 ± 2.1 = 71.9, 76.1

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Confidence Interval Example 90% CI: 74.0 ± 1.645(0.8) = 74.0 ± 1.3 = 72.7, 75.3 We can be 90% confident that the true population heart rate is between 72.7 and 75.3 bpm 95% CI: 74.0 ± 1.960(0.8) = 74.0 ± 1.6 = 72.4, 75.6 We can be 95% confident that the true population heart rate is between 72.4 and 75.6 bpm 99% CI: 74.0 ± 2.575(0.8) = 74.0 ± 2.1 = 71.9, 76.1 We can be 90% confident that the true population heart rate is between 71.9 and 76.1 bpm As we increase the level of confidence, the interval widens because the larger the range between the lower and upper bounds, the more confident we can be that the interval contains the true mean.

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Factors Affecting Width of a Confidence Interval Factors that determine the size of a standard error and therefore the width of a confidence interval: 1.The amount of variability in the sample: The greater the variability (for sample means, the larger the standard deviation), the wider the confidence interval. As sd increases, SE increases : When the variability among subjects in a sample is large, the variability among the means of repeated samples will also be large

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Factors Affecting Width of a Confidence Interval Factors that determine the size of a standard error and therefore the width of a confidence interval: 2.The sample size : The greater the sample size, the more narrow the confidence interval. As sample size increases, SE decreases: As more individuals in the population are sampled, the estimate of the population parameter will become more precise

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Sample Size and Confidence Intervals For the HR example: In our sample of 84 adults, we can be 95% confident that the that the true population heart rate is between 72.4 and 75.6 bpm If we increase our sample size to 500 adults, we can be 95% confident that the true population heart rate is between and bpm 95% CI: 74.0 ± 1.960(0.335) = 74.0 ± = 73.34,

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Confidence vs Precision The level of confidence reflects the uncertainty/variability inherent in sampling. More variability in the sample means that the standard deviation will be larger and therefore, the interval will be wider. This is not the same as increasing or decreasing your level of confidence. More variability in the sample means less accuracy that the sample statistic will accurately reflect the true population parameter (less precision) The wider the confidence interval, the lower the precision. Larger sample size = lower standard error = narrower CI (less variability)

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Confidence Intervals in Medical Research Confidence intervals (usually 95%) around sample means are commonly reported in published medical research. Other sample statistics that are commonly reported with confidence intervals include: difference between 2 means proportions differences between 2 proportions correlations relative risks odds ratios

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