One Sample t-test measures whether the population mean is statistically significantly different from a hypothesized value. For example : Doctors recommend to have low cholesterol level - ideal cholesterol level should be below 200. With the use of one-sample t-test we can examine if the average cholestrol level of the population is statistically different from 200.

$$ t = \frac{\bar{x}-\mu{}_{0}}{s_{\bar{x}}} $$

where

$$ s_{\bar{x}} = \frac{s}{\sqrt{n}} $$

\(\mu_{0}\) : Hypothesized value

\(\bar{x}\) : Sample mean

\(n\) : Sample size

\(s\) : Sample standard deviation

\(s_{\bar{x}}\) : Estimated standard error of the mean

In the calculation below you have two options - either to enter raw data or you can enter summary information which is required to calculate one sample t-test. Under raw data tab, you can enter values separated by comma, space, tab spaces or new line.

Enter Raw Data

*Paste data below from MS Excel or Notepad.*

Enter Summary Data

Step by Step Calculation

$$ s_{\bar{x}} = \frac{s}{\sqrt{n}} $$

Assumptions of One Sample t-test

- Data must be normally distributed
- Sample must be picked randomly from population
- Data must be continuous