Title: Confidence Interval - Fully Explained - ML+
Headings: Confidence Interval – Fully Explained
Confidence Interval – Fully Explained
You will know
1. What is Confidence Interval?
Let’s understand with an example
2. Two types of Confidence Intervals problems
Secondly, the approach you take to compute the confidence intervals depends on what information you know about the population.
3. Difference between Population parameter vs Sample statistic
4. Confidence Interval Formula
If population standard deviation is known, then:
If population standard deviation is not known, then:
5. Example 1: Estimating Confidence Interval when population standard deviation is not known
Exercises:
6. Example 2: Estimating Confidence Interval when the population standard deviation is known
How to interpret the z-table?
Since the formula has sqrt (n) in the denominator, the larger the number of samples you have, smaller will be the margin of error and thereby the smaller the confidence interval will be, which also means, more precise the estimate will be.
7. Confidence interval of a proportion
Problem Statement
Solution:
8. Bootstrapping in Statistics: How to Compute Confidence Interval with Bootstrapping
Bootstrapping is purely a sampling based technique, it can be used to estimate the confidence intervals regardless of what distribution your data follows.

Content: Confidence Interval - Fully Explained - ML+
Confidence Interval – Fully Explained
by Selva Prabhakaran |
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Confidence interval is a measure to quantify the uncertainty in an estimated statistic (like the mean) when the true population parameter is unknown. Training Custom Text Classification Model in spaCy. Photo by Jessica Wong. You will know
1. What is Confidence Interval? 2. Two types of Confidence Intervals problems
3. Difference between Population parameter vs Sample statistic
4. Confidence Interval Formula
5. Example 1: