That depends on your research question and hypothesis, among other factors. I have a Youtube video on choosing the right statistical test. Hope you find it useful.

https://www.youtube.com/watch?v=F4AKZ9-u_ic

Sign test after giving + or- for that object, or using paired t- test. Regards.

Few commonly used approaches:

1. **Descriptive Analysis**: Descriptive analysis provides a summary of the sensory data, including measures of central tendency, variability, and graphical representations. This analysis is helpful for gaining an overall understanding of the data. Descriptive statistics such as mean, standard deviation, and graphical plots like bar charts or boxplots can be used.

2. **Analysis of Variance (ANOVA)**: ANOVA is useful when comparing mean differences among different groups or treatments. If you have multiple factors in your sensory data, you can perform a factorial ANOVA to assess the main effects and interactions between factors. The `aov()` function in R can be used for conducting ANOVA.

3. **T-Tests**: T-tests are suitable for comparing means between two groups. If you want to compare the mean ratings on the 9-hedonic scale between two different conditions or groups, you can use either independent samples t-tests (for unpaired groups) or paired samples t-tests (for paired or repeated measures data).

4. **Non-Parametric Tests**: If your data violate the assumptions of parametric tests, or if you prefer to use non-parametric methods, you can consider non-parametric tests such as the Wilcoxon rank-sum test (Mann-Whitney U test) for comparing two groups or the Kruskal-Wallis test for comparing multiple groups.

Here is a basic script outline that you can follow for conducting an analysis using RStudio:

```R

# Load the necessary packages

library(ggplot2) # for plotting

library(dplyr) # for data manipulation

# Import and preprocess the data

data