# Confidence level

Confidence level is a statistic measure used to quantify the accuracy of a survey or test. It is a measure of how confident we can be that the results of a survey or test are representative of the true value of the population. It is expressed as a percentage, and is calculated by dividing the sample size by the population size. Generally, the higher the confidence level, the more accurate the survey or test results.

For example, a 95% confidence level in a survey means that if the survey were conducted 100 times, 95 times the results would fall within the same range. A 99% confidence level would indicate that 99 times out of 100, the survey results would be within the same range.

## Example of Confidence level

Let's say a survey is conducted to estimate the percentage of people who own a pet in a particular city. The survey is conducted on a sample size of 500 people. The population size of the city is 10,000.

The confidence level of this survey can be calculated as follows:

Confidence level = ${\displaystyle {\frac {500}{10,000}}}$ x 100 = 5%

Therefore, this survey has a confidence level of 5%, meaning that if the survey were conducted 100 times, the results would fall within the same range 95 times.

## Formula of Confidence level

The formula of confidence level is given by:

${\displaystyle Confidencelevel={\frac {samplesize}{populationsize}}\times 100}$

This formula is used to calculate the confidence level of a survey or test by taking the sample size and dividing it by the population size, then multiplying the result by 100 to get the confidence level as a percentage. The higher the confidence level, the more accurate the survey or test results.

The confidence level of a survey or test is important to consider when interpreting the results as it provides an indication of the accuracy of the results. A higher confidence level gives more assurance that the results are accurate, while a lower confidence level means that the results may not be as accurate.

## When to use Confidence level

Confidence level is most commonly used when conducting surveys or tests. It is used to quantify the accuracy of the survey or test results, and can be used to measure the reliability of the data collected. In addition, confidence level can be used to determine the sample size needed for a survey or test.

For example, if a survey requires a 95% confidence level, the sample size will need to be larger than if the survey only requires a 50% confidence level. This is because the higher the confidence level, the more reliable the survey results will be.

Confidence level is also used in hypothesis testing. Hypothesis testing is used to determine if a hypothesis is true or false based on a set of data. The higher the confidence level, the more reliable the data and the more likely the hypothesis is to be true.

## Range of Confidence level

• 95% Confidence Level: A 95% confidence level is the most commonly used. It means that 95 out of 100 times, the survey results will fall within the same range.
• 99% Confidence Level: A 99% confidence level is considered to be very reliable. It means that the survey results will fall within the same range 99 out of 100 times.
• 90% Confidence Level: A 90% confidence level is considered to be acceptable in most cases. It means that the survey results will fall within the same range 90 out of 100 times.

## Steps of Confidence level

• Determine the sample size: To calculate the confidence level, you first need to determine the sample size of the survey or test. The sample size should be large enough to provide an accurate representation of the population.
• Determine the population size: Next, determine the population size. This is the total number of people that are being sampled in the survey or test.
• Calculate the confidence level: Finally, calculate the confidence level by dividing the sample size by the population size, and multiplying the result by 100.

## Advantages of Confidence level

• Confidence level can provide a measure of the accuracy of a survey or test. It allows for comparison between surveys or tests, as it is a measure of how reliable the results are.
• It can be used to determine the sample size necessary to obtain accurate results.
• It gives us an indication of how close the survey results will be to the true population value.

## Disadvantages of Confidence level

• It does not take into account any sampling errors that may occur.
• It cannot account for any bias that may be present in the sample.
• It may not accurately reflect the accuracy of the results if the sample size is too small.

## Limitations of Confidence level

• Confidence level can only be estimated, and actual results may vary.
• Confidence level does not account for outliers or extreme values, which can affect the accuracy of survey results.
• Confidence level does not guarantee accuracy, as it only provides an estimate of the likely accuracy of a survey or test.

## Other approaches related to Confidence level

• Margin of Error: The Margin of Error is a measure of how much the survey results may differ from the true value of the population. It is calculated by multiplying the standard deviation of the sample by a multiplier, which is determined by the confidence level. This multiplier is known as the z-score.
• Confidence Interval: The Confidence Interval is a range of values that can be used to estimate the true value of the population with a certain level of confidence. It is calculated by subtracting and adding the Margin of Error to the sample mean.

Confidence level is a measure of accuracy used to determine the reliability of survey or test results. It is calculated by dividing the sample size by the population size, and expressed as a percentage. Other related approaches include the Margin of Error and the Confidence Interval, which are used to estimate the true value of the population.

 Confidence level — recommended articles Harmonic mean — Statistical significance — Kurtosis — Residual standard deviation — Probability density function — Nominal scale — C chart — Mann-Whitney U test — Sampling error