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