Chi-Squared Goodness-of-Fit Tests are useful tools that help us understand how well our actual data fits what we expect. Here’s a simple breakdown of how they work:

1. Observed vs Expected: First, you look at the numbers you collected (this is the observed counts) and compare them to what you expected to see (these are the expected counts).

2. Calculation: Next, you calculate something called the Chi-Squared statistic. You can use this simple formula: $\chi^2 = \sum \frac{(O - E)^2}{E}$ In this formula, $O$ stands for the observed frequency (the numbers you collected), and $E$ is the expected frequency (the numbers you thought you would see).

3. Significance: Lastly, you check your calculated number against a special number found in the Chi-Squared distribution table. This depends on something called degrees of freedom, which helps you know how to interpret the results. If your number is larger than this special number, it means that your actual data probably doesn’t match the expected data very well.

This process helps in understanding if what we see in our data is what we expected!