Two terms that often confuse students in statistics classes are **t-scores** and **z-scores**.

Both are used extensively when performing hypothesis tests or constructing confidence intervals, but they’re slightly different.

Here’s the formula for each:

**t-score = (x – μ) / (s/√n)**

where:

**x**: Sample mean**μ**: Population mean**s**: Sample standard deviation**n**: Sample size

**z-score = (x – μ) / σ**

where:

**x**: Raw data value**μ**: Population mean**σ**: Population standard deviation

This flow chart shows when you should use each, depending on your data:

The following examples show how to calculate a t-score and z-score in practice.

**Example 1: Calculating a T-Score**

Suppose a restaurant makes burgers that claim to have a mean weight of μ = 0.25 pounds.

Suppose we take a random sample of n = 20 burgers and find that the sample mean weight is x = 0.22 pounds with a standard deviation of s = 0.05 pounds. Perform a hypothesis test to determine if the true mean weight of all burgers produced by this restaurant is equal to 0.25 pounds.

For this example, we would use a **t-score** to perform the hypothesis test because neither of the following two conditions are met.

- The population standard deviation (σ) is known. (σ is not provided in this example)
- The sample size is greater than 30. (n = 20 in this example)

Thus, we would calculate the t-score as:

- t-score = (x – μ) / (s/√n)
- t-score = (.22 – .25) / (.05 / √20)
- t- score = -2.68

According to the T Score to P Value Calculator, the p-value that corresponds to this t-score is **0.01481**.

Since this p-value is less than .05, we have sufficient evidence to say that the mean weight of burgers produced at this restaurant is not equal to 0.25 pounds.

**Example 2: Calculating a Z-Score**

Suppose a company manufactures batteries that are known to have a lifespan that follows a normal distribution with a mean of μ = 20 hours and a standard deviation of σ = 5 hours.

Suppose we take a random sample of n = 50 batteries and find that the sample mean is x = 21 hours. Perform a hypothesis test to determine if the true mean lifespan of all batteries manufactured by this company is equal to 20 hours.

For this example, we would use a **z-score** to perform the hypothesis test because the following two conditions are met:

- The population standard deviation (σ) is known. (σ is equal to 5 in this example)
- The sample size is greater than 30. (n = 50 in this example)

Thus, we would calculate the z-score as:

- z-score = (x – μ) / σ
- z-score = (21 – 20) / 5
- z- score = 0.2

According to the Z Score to P Value Calculator, the p-value that corresponds to this z-score is **0.84184**.

Since this p-value is not less than .05, we don’t have sufficient evidence to say that the mean lifespan of all batteries manufactured by this company is different than 20 hours.

**Additional Resources**

The following tutorials offer more information on both t-scores and z-scores:

Normal Distribution vs. t-Distribution: What’s the Difference?

How to Read the t-Distribution Table

How to Read the Z Table