STATS 101: Understanding Confidence level and Interval so that you NEVER forget it

Anmol Tomar
4 min readMar 12, 2024
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Imagine you’re an explorer, embarking on a quest not through dense jungles or uncharted territories, but through the intricate world of statistics. Your mission? To uncover the secrets of confidence levels and intervals. These concepts, often shrouded in academic jargon, are the compass and map that guide researchers through the vast landscape of data. But fear not! This journey is designed to make these statistical tools not just understandable but unforgettable.

The Essence of Confidence

Before we dive into the depths, let’s start with the heart of the matter: what are confidence levels and intervals? Picture this scenario: You’re the owner of a magical orchard where fruits tell you how sweet they are. However, with millions of fruits, it’s impossible to ask each one. So, you decide to taste a sample to estimate the sweetness of all fruits in your orchard.

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Confidence Level: The Trust in Your Taste Buds

The confidence level is like your trust in your taste buds to accurately gauge the sweetness of the fruits. In statistics, it’s a measure of how sure you are that your sample reflects the entire orchard. Expressed as a percentage (commonly 95%), it represents how confident you are that if you were to repeat your tasting with different samples, your estimate would fall within a certain range most of the time.

Confidence Interval: The Range of Sweetness

Now, imagine after tasting a handful of fruits, you declare, “I’m 95% confident the average sweetness of all fruits in my orchard falls between 6.5 to 7.5 on the sweetness scale.” This range (6.5 to 7.5) is your confidence interval. It’s not about the probability of the actual sweetness level being in this range, but about how confident you are in your sampling process to estimate the average sweetness accurately.

The Journey to Understanding

Why Do We Need Them?

In the real world, data scientists and researchers use confidence levels and intervals to make informed decisions based on samples, because, like our magical orchard, it’s often impractical or impossible to measure the entire population.

The Path to Calculation

Calculating a confidence interval involves a few steps:

  1. Select a sample from your population (like tasting a few fruits from the orchard).
  2. Calculate the sample mean (average sweetness from your tasting).
  3. Determine the standard deviation (how much the sweetness varies among your sample).
  4. Decide on your confidence level (how sure you want to be about your estimate).
  5. Use the below formula to calculate the confidence interval based on the above information.
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The formula adjusts based on the size of your sample and whether you know certain population parameters, but the essence remains: it provides a range that, given your confidence level, likely contains the true average sweetness of the orchard.

The Adventure’s Rewards

Understanding confidence levels and intervals empowers you to interpret and question research findings critically. For example, when a study says, “We’re 95% confident the average time spent on social media is 2 to 2.5 hours daily,” you now understand that if the study were repeated many times, 95 out of 100 of those studies would estimate the average within that interval.

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A Treasure to Keep

Our expedition concludes, but the treasure of knowledge you’ve acquired will illuminate your path in the vast world of data. Confidence levels and intervals, once mystical concepts, are now tools in your exploratory kit. Remember, they don’t tell us the exact truth but guide us in estimating it, armed with statistical confidence.

In summary, confidence levels express our trust in the consistency of our estimates across multiple samples, while confidence intervals provide a range that, with a certain level of confidence, encompasses the true average of our population.

I hope you learned something new from the blog.

Happy Learning!

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Anmol Tomar

Top AI writer | Data Science Manager | Mentor. Want to kick off your career in Data Science? Get in touch with me: