I come from a school that uses Google Apps for Education, so a lot of the information presented below will be specific to Google Sheets, however, there is a lot of crossover in the functionality of Sheets, Excel and Numbers so hopefully even if you are an Office or Mac person you will be able to gain from the tutorials below.
Cleaning Up the Data
Inserting a Chart
Including a Trendline
Here is a template that you can use as the basis for writing a scientific report. For more information on how to properly display data, just open it and scroll down to the section titled 'Displaying Data'. There you will find a little blue question mark that links to all of the information you will need to display your data correctly.
Precision and accuracy are two words that are easily confused, but their meanings in science have very important differences when used to analyse data.
Precision is the ability to repeat an experiment and get the same results. Accuracy is how close your result is to what could be considered the 'true' result.
The image below is a fairly common way to represent the difference between precision and accuracy if we consider that the middle of the bulls-eye represents the 'true' result.
Identifying Precision and Accuracy in Experimental Results No scientific investigation is ever perfect, so it is the job of the scientist to estimate how imperfect their results are. This is why it is important to be able to understand the precision and accuracy of your investigation. Here are the things to look for:
Precision: 1. The range of your results. Imagine you took five measurements of the temperature of a glass of water. The results were 25°C, 28°C, 22°C, 30°C and 24°C. The lowest temperature measurement obtained was 22°C and the highest was 30°C which gives us a range of 8°C. We are not able to get the same result every time which means our measurements are not precise. 2. The uncertainty of your equipment. Sometimes you might get the same result every time, but it could be because the precision of your equipment is poor. If you use a mass balance that does not measure decimal places, we might get two measurements that both say 50g. But if we were then to measure the same masses on a balance that measures to two decimal places, we could find that one mass was in fact 49.51 and the other was 50.49. This is why we must consider our equipment as an influence on precision.
Accuracy: 1. Closeness to the expected result. Sometimes in a science class students carry out experiments that have been done before in order to learn a concept. This means that an accepted scientific result exists that we can compare our own results to. If, for example, you were conducting an investigation into the chemical formula for magnesium oxide, which we know has a ratio of 1:1, and you obtained a result of 1.3:1, then we can say that your result is not accurate. 2. Correlation to the trendline. If you have a trendline on your graph and the data points do not fit it very well, you can say that your data is not very accurate. However, this does not necessarily mean that data that closely matches a trendline is accurate (see point 3 below for an explanation). It can also be argued that a poor trendline fit might be caused by low precision and not low accuracy. You would need to consider the number of trials in deciding which is the cause (more trials leads to more accuracy). 3. The value of the y intercept. If you are conducting an investigation in which you expect the trendline to pass through (0, 0) and it doesn't this could indicate that your experiment has a systematic error (one that you do the same way every time). This is why a close fit to a trendline does not automatically mean high accuracy.
Science is based on evidence, and the best evidence is hard data. It is true that data can be manipulated or misrepresented in the hands of the wrong person, but if you have the skills for interpreting data yourself, you will be better positioned to find the truth. When analysing any kind of data, here are some key points to think about:
How accurate is the data?
Is the sample size large enough?
Do the numbers show a significant result?
Have enough variables been controlled?
Does the experiment represent a real life situation?
Do the results of the experiment have applications in multiple areas?
How could the experiment be improved?
To help guide you through these thought processes in more detail, here are links to three documents with specific examples and detailed checklists:
Evaluating a Hypothesis - This document will walk you through considering the accuracy and precision of data. Evaluating a Method - This document will help you to identify the steps in a method that reduced accuracy, as well as to consider whether the experiment has applications in the real world. Improvements and Extensions - This document shows how you can use the method limitations to suggest improvements and extensions to an experiment.
Several methods exist for identifying outliers in data and the correct method will depend on the type of investigation you are conducting. That said, here are three methods you can use for identifying outliers in your data:
Find the median of the data. Determine whether any data points fall outside ± 2 times the uncertainty value. If only one or two do, eliminate them as outliers by striking through them (cmd+shift+x on a Mac to strike through text).
Determine the median value and the interquartile range for your data set. Any data points that are outside the median ± 1.5 x IQR are outliers. Here is a link to a Kahn Academy lesson explaining how to do this.
Calculate the mean and standard deviation of your data. Any data points that lie more than three standard deviations away from the mean should be eliminated.
If you would like to take a shortcut, you can use this Google Sheet in which I've done the heavy lifting for you.