Confidence Intervals in Inferential Statistics
A confidence interval is a range of values, derived from sample data, that is likely to contain the true population parameter. It is used to quantify the uncertainty or margin of error associated with a statistical estimate. For example, if you have a sample mean and want to estimate the population mean, you can calculate a confidence interval around the sample mean. A 95% confidence interval means that if you were to take 100 different samples and calculate a confidence interval for each sample, about 95 of the 100 intervals would contain the true population mean.
The width of the confidence interval is affected by the level of confidence you choose (e.g., 95%, 99%), the variability of the data, and the sample size. A wider interval indicates more uncertainty, while a narrower interval indicates more confidence in the estimate.
What is Inferential Statistics?
In the world of data analysis, statistics plays a big role in helping us understand patterns and insights from raw data. Descriptive statistics help us summarize and describe data, while inferential statistics take us a step further by letting us make predictions and decisions about a larger group based on a smaller sample.
In this article, we’ll dive into inferential statistics, looking at why it’s important, how it works, and where it’s used.
Contact Us