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In biostatistics, a confidence interval (CI) is a range of values, derived from the statistics of the observed data, that is likely to contain the value of an unknown population parameter. In other words, it gives an estimated range of values which is likely to include the parameter of interest. Confidence intervals are used to indicate the reliability of an estimate. For example, if you were estimating the average height of a population based on a sample, a 95% confidence interval might indicate that you are 95% certain the true average height of the entire population falls within this interval. The actual calculation of a confidence interval depends on the distribution of the data (often assumed to be normally distributed), the size of the sample, and the level of confidence desired. The “confidence level“ (commonly set at 95%, but other values like 90% or 99% can also be used) indicates the probability that the calculated confidence interval actually includes the true parameter value. It's important to understand that the confidence interval does not predict that the true parameter has a specific probability of being inside the calculated interval in repeated samples; rather, it means that if we were to take many samples and calculate a confidence interval for each, a specified proportion (e.g., 95%) of these intervals would contain the true parameter value. Confidence intervals are a fundamental concept in inferential statistics, allowing researchers to make estimates about population parameters based on sample data, while also accounting for variability in their estimates. Problems: Which of the following is/are nominal data? A) Sex B) Blood group C) Race D) A, B and C * The mean decrease in Heart Rate after initiating a beta blocker XYZ in 90 patients was 24 beat per minute with standard error of beats for minute. What would be the 95% confidence interval for the decrease in HR after initiating drug XYZ? A) - 31.4 mmHg * B) - mmHg C) - mmHg D) - mmHg
