Confidence intervals (CIs) are a fundamental concept in data science. In this informative guide, we’ll delve into the world of confidence intervals using an intuitive example to help you grasp this concept with confidence.
The Bus Stop Scenario:
Imagine yourself at a bus stop where the expected arrival time of the bus is usually 9:30 am. However, as you may have noticed, the bus’s arrival time can vary. Another person joins you at the stop and asks, “Based on your experience, what percentage of the time does the bus arrive between 9:25 am and 9:35 am?”
You ponder the question and reply, “90% of the time.” The person then inquires further, “How about between 9:20 am and 9:40 am?” You respond, “95% of the time.” This scenario forms the core logic
behind confidence intervals.
Understanding Confidence Levels:
Confidence intervals offer an estimated range of values based on specific confidence levels. In our
discussion, we’ve used 90% and 95% as confidence levels, with 95% being the more commonly
employed option. Occasional use cases involve confidence levels like 90% and 99%.
A) 95% Confidence Interval:
For a 95% Confidence Interval, we get [9:20 am – 9:40 am], which translates to 9:30 am ± 10 minutes. To aid comprehension, a visual representation is provided below (note that confidence intervals differ from prediction intervals, and these graphics are for explanatory purposes only).
B) 90% Confidence Interval:
For a 90% Confidence Interval, the range narrows to [9:25 am – 9:35 am], or 9:30 am ± 5 minutes.
Formula for Confidence Interval::
In our 95% Confidence Interval of 9:30 am ± 10 minutes, the 10-minute range is known as the margin of error.
This margin of error stems from three key factors: 1) the z-value, 2) the standard deviation, and 3)
the sample size. As such, the confidence interval is directly proportional to the standard deviation and inversely proportional to the sample size.
Here are some essential points to remember about confidence intervals:
Higher confidence levels result in wider confidence intervals; for instance, a 90% CI is narrower than a
95% CI, and a 99% CI is the widest.
Greater variability in the sample data leads to wider confidence intervals.
When keeping other factors constant, a larger sample size results in a narrower confidence interval.
Proficiency in calculating and interpreting confidence intervals is a valuable skill for any data scientist.
In conclusion, understanding confidence intervals empowers data scientists to make more informed
decisions and draw meaningful insights from data with a quantifiable level of certainty.