Answer:
Error bars are graphical representations of the variability of data and used on graphs to indicate the error or uncertainty in a reported measurement. They can be generated from the absolute uncertainty, which is the size of the error in the same units as the measurement.
Error bars can be used to analyse data in different ways, such as:
• Comparing the precision and accuracy of different measurements or methods. Precision is how close the measurements are to each other, and accuracy is how close they are to the true value. Smaller error bars indicate higher precision and accuracy, while larger error bars indicate lower precision and accuracy.
• Assessing the significance and reliability of the results. Significance is how likely the results are due to a real effect rather than chance, and reliability is how consistent and repeatable the results are. If the error bars of two measurements overlap, it means that there is no significant difference between them at a certain confidence level, and that the results are not reliable. If the error bars do not overlap, it means that there is a significant difference between them at a certain confidence level, and that the results are more reliable.
• Estimating the uncertainties in derived quantities, such as the gradient and intercept of a straight-line graph. Uncertainties in derived quantities can be calculated using various methods, such as the maximum-minimum method or the least-squares method. These methods use the error bars of the data points to find the range or standard deviation of the derived quantity.
Therefore, error bars generated from the absolute uncertainty can be useful tools to analyse data and communicate the quality and validity of the measurements.
Step-by-step explanation: