Two terms that are often used in statistics are sample proportion and sample mean.
Here’s the difference between the two terms:
Sample proportion: The proportion of observations in a sample with a certain characteristic.
Often denoted p̂, It is calculated as follows:
p̂ = x / n
- x: The number of observations in the sample with a certain characteristic.
- n: The total number of observations in the sample.
Sample mean: The average value in a sample.
Often denoted x, it is calculated as follows:
x = Σxi / n
- Σ: A symbol that means “sum”
- xi: The value of the ith observation in the sample
- n: The sample size
Sample Proportion vs. Sample Mean: When to Use Each
The sample proportion and sample mean are used for different reasons:
Sample proportion: Used to understand the proportion of observations in a sample that have a certain characteristic.
For example, we could use the sample proportion in each of the following scenarios:
- Politics: Researchers might survey 500 individuals in a certain city to understand what proportion of residents support a certain candidate in an upcoming election.
- Biology: Biologists may collect data on 100 sea turtles to understand what proportion of them have experienced damage from pollution.
- Sports: A journalist may survey 1,000 college basketball players to understand what proportion of them shoot left-handed.
Sample mean: Used to understand the average value in a sample.
For example, we could use the sample mean in each of the following scenarios:
- Demographics: Economists may collect data on 5,000 households in a certain city to estimate the average annual household income.
- Botany: A botanist may take measurements on 50 plants from the same species to estimate the average height of the plant in inches.
- Nutrition: A nutritionist may survey 100 people at a hospital to estimate the average number of calories that residents eat per day.
Depending on the question of interest, it might make more sense to use the sample proportion or the sample mean to answer the question.
Using the Sample Proportion & Sample Mean to Estimate Population Parameters
Both the sample proportion and the sample mean are used to estimate population parameters.
Sample Proportion as an Estimate
We use the sample proportion to estimate a population proportion. For example, we might be interested in understanding what proportion of residents in a certain city support a new law.
Since it would be too costly and time-consuming to survey all 20,000 residents in the city, we instead survey 500 and calculate the proportion of residents in the sample who support the new law.
We then use this sample proportion as our best estimate of the proportion of residents in the entire city who suppose the new law. However, since it’s unlikely that our sample proportion exactly matches the population proportion, we often use a confidence interval for a proportion – a range of values that we believe contains the true population proportion with a certain level of confidence.
Sample Mean as an Estimate
We use the sample mean to estimate a population mean. For example, we might be interested in understanding the average height of a certain species of plants.
Since it would be too costly and time-consuming to measure the height of all 10,000 plants in a certain region, we instead measure the height of 150 plants and use the sample mean as our best estimate of the population mean.
However, since it’s unlikely that our sample mean exactly matches the population mean, we often use a confidence interval for a mean– a range of values that we believe contains the true population mean with a certain level of confidence.
Confidence Interval for Proportion Calculator
Confidence Interval for Mean Calculator