![]() They provide a way to quantify the uncertainty in an estimate by specifying a range of plausible values for the parameter of interest. This parameter could be, for example, a mean, a proportion, or a difference between two means.Ĭonfidence intervals are commonly used to estimate population parameters based on sample data. In statistics, a confidence interval is a range of values likely to contain the true population parameter with a certain degree of confidence. It is a relatively wide range and suggests significant uncertainty in our estimate. The Z-score of 2.33 indicates that this range extends approximately two and third standard deviations from the mean. In other words, if we assume a normal distribution, we can be 98% confident that the true population parameter falls within the range defined by our confidence interval. It means there is a 98% probability that a data point will fall within 2.33 standard deviations of the mean. For a 98% confidence interval, the critical value is approximately 2.33. We can find the critical value using a Z-table or a statistical calculator. The critical value is the number of standard deviations from the mean corresponding to the desired confidence level. To find the Z-score for a 98% confidence interval, we must first determine the corresponding critical value for the Z-distribution. Z-scores determine the probability of a data point occurring within a certain range of values. The Z-score calculates the number of standard deviations that a data point deviates from the mean of the distribution. ![]() ![]() It is critical to consider the Z-score when conducting calculations. We need to use a formula that considers the sample size, standard deviation, and the desired confidence level to calculate a confidence interval. The confidence level is typically expressed as a percentage of 95% or 98%. ![]()
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