To measure the uncertainty associated with a set of results you need to repeat measurements.
Accuracy: how close to the “true” value you are (e.g. how close to your true weight your bathroom scales weigh you to).
Your data may not fit your pet idea, or even the generally accepted scientific theory, but that does not they are wrong.
For most measurements in analytical chemistry some form of calibration curve is required. The better the calibration the more accuracy and precise are the results that you can achieve.
When you run an analysis there is a procedure you need to follow to ensure you get the best data.
Before you chose a piece of equipment or experimental method you need to ensure that it can detect the thing you want to measure (analyte) in the concentrations present. For example, can your method detect 1ppb phosphate in sea water? If you can’t measure it then there is no point in making, or collecting, it!
While Excel is anything but ideal for use in statistical work it can do many amazing things. However, it can be quite opaque as to what is actually happening and difficult to find the correct function. For this reason I have picked out the key functions for the statistical analysis discussed here.
In an experiment you may hypothesize there is a relationship between two values or two sets of values. To ensure that relationship is robust you need carry out a test of the significance of that hypothesized relationship.
A diagram which explores the scientific method and details around that.