Confidence Interval

Introduction to Confidence Interval  

In statistics, making estimates from sample data is essential. A confidence interval gives a range of values that likely contains an unknown population parameter. For instance, rather than simply stating that the average height of a population is 5.6 feet, a confidence interval might indicate that the average falls between 5.4 and 5.8 feet, with a certain level of confidence.  

So, what is a confidence interval? It is a range derived from sample data that estimates an unknown population value with a set level of confidence, such as 90%, 95%, or 99%. This allows statisticians, researchers, and scientists to make informed decisions based on data.  

 

Table of Contents  

• Confidence Interval Definition
• Confidence Interval Formula
• Confidence Interval Table
• How to Calculate Confidence Interval
• Confidence Interval Example
• Why Confidence Interval Matters
• Conclusion
• FAQs On Confidence Interval

 

Confidence Interval Definition  

Let’s dive into the definition of a confidence interval.  

A confidence interval is a type of interval estimate calculated from the statistics of observed data. It provides a range of plausible values for an unknown population parameter. The interval is associated with a confidence level that describes the certainty that the parameter lies within it.  

In simple terms, what is confidence interval? It’s a range within which we believe the true value of a population parameter lies. The confidence interval definition plays a crucial role in confidence interval statistics as it allows statisticians to express uncertainty in sample estimates..  

 

Confidence Interval Formula  

To understand how to find confidence interval, first look at the formula:  

 

For a Population Mean (When Population Standard Deviation is Known):  

Confidence Interval = x̄ ± Z * (σ / √n)  

Where:  

• x̄ = Sample mean  

• Z = Z-value from the standard normal distribution corresponding to the desired confidence level  

• σ = Population standard deviation  

• n = Sample size  

 

For a Population Mean (When Population Standard Deviation is Unknown):  

Confidence Interval = x̄ ± t * (s / √n)  

Where:  

• t = t-value from Student’s t-distribution  

• s = Sample standard deviation  

Understanding the confidence interval formula is essential in confidence interval statistics as it forms the base for accurate statistical estimation.

 

Confidence Interval Table  

The following table displays standard Z-values used in confidence interval statistics:  

 

 

This table aids in determining the Z-value for the confidence interval formula based on the desired confidence level.  

So, what is a confidence interval Z-score? It's the number of standard deviations from the mean that corresponds to a specific confidence level.  

 

How to Calculate Confidence Interval  

Let’s break down how to calculate a confidence interval step by step:  

 

Step 1: Identify the sample mean (x̄). 

Calculate the average value of the sample.  

 

Step 2: Find the standard deviation.
If the population standard deviation (σ) is known, use it. Otherwise, use the sample standard deviation (s).  

 

Step 3: Select the confidence level.
Common levels include 90%, 95%, and 99%.
Use the confidence interval table to find the corresponding Z or t-value.  

 

Step 4: Use the confidence interval formula.
Plug the values into the correct formula (Z or t-based).  

 

Step 5: Interpret the result.
The final result will be an interval.

For example: "With 95% confidence, the population mean lies between 50.4 and 54.6."  

 

This five-step process clearly outlines how to find confidence interval in a practical manner.  

Understanding how to calculate confidence interval will improve your grasp of confidence interval statistics and data analysis.

 

Confidence Interval Example  

Example 1: Confidence Interval for Population Mean (Known σ)  

Problem:  

A sample of 64 batteries has an average life of 120 hours. The population standard deviation is 8 hours. Find the 90% confidence interval for the population mean.

Given:  

• n = 64  

• x̄ = 120  

• σ = 8  

• Z for 90% = 1.645  

Solution:  

Confidence Interval = x̄ ± Z × (σ / √n)  

= 120 ± 1.645 × (8 / √64)  

= 120 ± 1.645 × (8 / 8)  

= 120 ± 1.645  

Answer:  Confidence Interval = (118.355, 121.645)  

Interpretation: With 90% confidence, the population mean battery life is between 118.355 and 121.645 hours.  

 

Example 2: Confidence Interval with Small Sample (Unknown σ, Use t-distribution)  

Problem:  

A sample of 25 students has an average test score of 75 with a sample standard deviation of 5. Find the 95% confidence interval for the population mean.

Given:  

• n = 25  

• x̄ = 75  

• s = 5  

• Degrees of freedom = 24  

• t-value for 95% and df=24 ≈ 2.064  

Solution:  

Confidence Interval = x̄ ± t × (s / √n)  

= 75 ± 2.064 × (5 / √25)  

= 75 ± 2.064 × (5 / 5)  

= 75 ± 2.064  

Answer:  Confidence Interval = (72.936, 77.064)  

Interpretation:  We are 95% confident that the true mean test score lies between 72.936 and 77.064.  

 

Example 3: Confidence Interval for Proportion  

Problem:  

Out of a random sample of 200 people, 130 said they prefer tea over coffee. Find the 95% confidence interval for the proportion of tea drinkers.

Given:  

• Sample proportion (p̂) = 130/200 = 0.65  

• n = 200  

• Z for 95% = 1.96  

Solution:  

Standard Error (SE) = √[p̂(1 - p̂)/n]  

= √[(0.65)(0.35)/200]  

= √[0.2275/200]  

= √0.0011375 ≈ 0.0337  

Margin of Error = 1.96 × 0.0337 ≈ 0.0661  

 

Confidence Interval = 0.65 ± 0.0661  

= (0.5839, 0.7161)  

 

Answer:  Confidence Interval = (0.5839, 0.7161)  

Interpretation:  With 95% confidence, between 58.39% and 71.61% of the population prefers tea over coffee.

This example illustrates how to find confidence interval using the formula and table.  

 

Why Confidence Interval Matters  

Understanding what a confidence interval is essential because it helps:  

• Quantify uncertainty in sample estimates  

• Inform decisions in scientific research, business, medicine, and more  

• Support or reject hypotheses with statistical evidence  

• Provide a visual sense of estimation precision  

The concept of confidence intervals is fundamental in statistics, and it is used across various fields to represent estimation accuracy. 

 

Conclusion  

In summary, a confidence interval is a valuable statistical tool. It offers a measure of reliability for estimated values. Knowing what is a confidence interval is and how to use the formula allows you to interpret and present data more accurately. From the definition to real-life applications, this concept enhances decision-making across disciplines.  

Learning how to find confidence interval using the table is an essential skill for anyone involved in data, research, or academia. Mastering this helps you unlock the full potential of statistics.  

 

Frequently Asked Questions on Confidence Interval

1. What does a 95% confidence interval mean?

Ans: A 95% confidence interval means that if you were to repeat an experiment multiple times, about 95% of the calculated intervals would contain the true population parameter.

2. What does 0.05 confidence interval mean?

Ans: A 0.05 confidence level corresponds to a 95% confidence interval. It means there's a 5% risk that the true parameter lies outside the interval.

3. What is confidence in simple words?

Ans: Confidence, in statistics, refers to how sure we are that a value (like a mean or proportion) falls within a specific range.

4.How to define confidence interval?

Ans: A confidence interval is a range of values, derived from sample data, that is likely to contain the true value of an unknown population parameter.

 

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