# r – Basic – T-Test -> Grouping Factor Must have Exactly 2 Levels

## r – Basic – T-Test -> Grouping Factor Must have Exactly 2 Levels

are you doing this:

```
t.test(y~x)
```

when you mean to do this

```
t.test(y,x)
```

In general use the `~`

then you have data like

```
y <- 1:10
x <- rep(letters[1:2], each = 5)
```

and the `,`

when you have data like

```
y <- 1:5
x <- 6:10
```

I assume youre doing something like:

```
y <- 1:10
x <- rep(1,10)
t.test(y~x) #instead of t.test(y,x)
```

because the error suggests you have no variation in the grouping factor `x`

The differences between ~ and , is the type of statistical test you are running. ~ gives you the mean differences. This is for dependent samples (e.g. before and after). , gives you the difference in means. This is for independent samples (e.g. treatment and control). These two tests are not interchangeable.

#### r – Basic – T-Test -> Grouping Factor Must have Exactly 2 Levels

I was having a similar problem and did not realize given the size of my dataset that one of my ys had no values for one of my levels. I had taken a series of gene readings for two groups and one gene had readings only for group 2 and not group 1. I hadnt even noticed but for some reason this presented with the same error as what I would get if I had too many levels. The solution is to remove that y or in my case gene from my analysis and then the error is solved.

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