One-sample and paired-sample *t*-test

`h = ttest(x)`

`h = ttest(x,y)`

`h = ttest(x,y,Name,Value)`

`h = ttest(x,m)`

`h = ttest(x,m,Name,Value)`

```
[h,p] =
ttest(___)
```

```
[h,p,ci,stats]
= ttest(___)
```

returns
a test decision for the null hypothesis that the data in `h`

= ttest(`x`

)`x`

comes
from a normal distribution with mean equal to zero and unknown variance,
using the one-sample *t*-test.
The alternative hypothesis is that the population distribution does
not have a mean equal to zero. The result `h`

is `1`

if
the test rejects the null hypothesis at the 5% significance level,
and `0`

otherwise.

returns
a test decision for the paired-sample `h`

= ttest(`x`

,`y`

,`Name,Value`

)*t*-test with
additional options specified by one or more name-value pair arguments.
For example, you can change the significance level or conduct a one-sided
test.

returns
a test decision for the one-sample `h`

= ttest(`x`

,`m`

,`Name,Value`

)*t*-test with
additional options specified by one or more name-value pair arguments.
For example, you can change the significance level or conduct a one-sided
test.

Use

`sampsizepwr`

to calculate:The sample size that corresponds to specified power and parameter values;

The power achieved for a particular sample size, given the true parameter value;

The parameter value detectable with the specified sample size and power.

`sampsizepwr`

| `ttest2`

| `ztest`