Question

explain why it is better to the data from the entire class averaged together when assessing results or creating a graph, rather than using only your own data

Answer 1

When assessing results or creating a graph, it is generally better to use data from the entire class averaged together, rather than just your own data, because doing so provides a more accurate representation of the overall performance of the class.

Using only your own data can be misleading because it may not be representative of the entire class. Your data may be an outlier, meaning it is significantly different from the rest of the data. This could be due to a variety of factors such as measurement error, sampling bias, or even just random chance.

By using data from the entire class and averaging it together, you are able to smooth out any individual variations and get a more accurate picture of the overall performance of the class. This helps to reduce the impact of outliers and provides a more reliable representation of the data.

Additionally, using the entire class data allows you to identify any trends or patterns in the data that may not be apparent from just your own data. This can help you to draw more meaningful conclusions and make better-informed decisions.

In summary, using data from the entire class averaged together when assessing results or creating a graph provides a more accurate and reliable representation of the overall performance of the class and helps to reduce the impact of outliers.

Answer 2

Using data from the entire class averaged together provides a more representative and accurate picture of the overall results when assessing or creating a graph. It minimizes the impact of individual variations and errors, leading to more reliable analysis.

Step-by-step explanation:

When assessing results or creating a graph, it is better to use data from the entire class averaged together rather than relying solely on your own data. This is because using the entire class's data provides a more representative and accurate picture of the overall results. By averaging the data from multiple students, you minimize the impact of individual variations and errors, which can lead to more reliable analysis and graphing.

For example, if you were assessing the average test scores of the class, using your own data might not give you an accurate representation of the class's performance as a whole. Some students may have done exceptionally well or poorly, skewing the results. By averaging the scores of the entire class, you eliminate the influence of outliers and get a more reliable measure of the class's performance.