T-Test Experiment
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Lets assume we want to evaluate if there is a difference in assessing the quality of a single stimuli between Expert listeners and Non-Expert listeners. Further we are interested if there is a significant difference between both *independent* groups.

# Power Analyis

1. In a previously done pilot test with only 6 participants (3 for each group) two means were estimated with m(expert) = 25.5 and and m(nonexpert) = 50 and a common standard deviation of 13.5.

```{r}
mexprt  <- 25.5
mnexprt <- 50.0
s  <- 13.5
```

2. calculate the effect size cohen d

```{r}



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3. What is the sample size n one would need for a t-test with a power of 95 percent, a significance level of .05 and the effect size previously calculated. Use (?power.t.test).

```{r}

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#  Exploratory statistics of the data

Now we assume the experiment has already been conducted. The data that has been collected consists of only two variables:
a *Score* which has been collected for a music stimulus and the level of professionalism of the listener *profession*.

Now we load the real data:

```{r}
data <- read.csv("data/experts.csv");
```

Do a graphical analysis here to gain more information:

4. Plot histograms of the data:

```{r}

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5. Generate Boxplots to show the differences in the performance between experts and non-experts. Use the formula syntax in (?formula). Answer the results in your own words.

```{r}

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6. Check for normality by using a graphical analysis (?qqnorm, ?qqline) AND a statistical test of your choice.

```{r}



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# T-Test

7. To test the hypothesis with a t-test should it be one or two sided? Why?

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8. Test for homogeneity of variances by using the Fligner Test (?fligner.test) and report the outcome of the test.

```{r}

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9. Compute the independent T-test using t.test and report the outcome in terms of the hypothesis in text.


```{r}
# assuming equal variances

```
