HOW SHOULD I ORGANIZE AND ANALYZE MY RESULTS?
Once you have recorded your data and observations in your science fair logbook, you will need to organize data in a way that makes sense.
You will then need to analyze your results to see if they provide any insight or possible solutions to your inquiry question.
This section of the learning will help support you through the process of organizing, displaying, analyzing, and discussing.
You will then need to analyze your results to see if they provide any insight or possible solutions to your inquiry question.
This section of the learning will help support you through the process of organizing, displaying, analyzing, and discussing.
ORGANIZING YOUR DATA
How should I organize my data?
 Your raw data and observations should not be presented anywhere other than in you science fair logbook.
 While raw data from each trial may show potential trends, you need to be certain that your results you were not observed by chance.
 You need to determine if your results have any mathematical value.
 The starting point is usually to group the raw data into categories, and/or to visualise it.
 The raw data from each individual trial should be "combined" with data from other "identical" trials.
 One of the most common techniques used for summarising is using graphs, particularly bar charts, which show every data point in order, or histograms, which are bar charts grouped into broader categories.
 To do this you will combine the like data points from each trial and calculate measures of Central Tendency (Mean, Mode, Median)
 Mean  The mathematical average of the values. The mean is found by adding up all of the given data and dividing by the number of data entries.
 Mode  The mathematical value that occurs the most often.
 Median  The middle value. To fin d the median, arrange the numbers in order from lowest to highest, then find the middle number by crossing off the numbers until you reach the middle point.
 Once you have calculated the measures of Central Tendency then create a new table showing this information,
 This is why it is important to run multiple trials of your investigation (the more trials you run, the more certain you can be that your results valid).
 Please remember to show correct units in your new data table.
DISPLAYING YOUR DATA
How should I display my data?
How do I creating a graph?

Spreadsheet and Graphing Tips 
STATISTICS
ANALYZE AND DISCUSS RESULTS
What do you do once you have organized your results, and analyzed them?
What can you conclude about your results?
Look for patterns and trends in your data?
What can you conclude about your results?
Look for patterns and trends in your data?
 What do you notice?
 Are you number increasing?
 Are you numbers decreasing?
SOURCES OF ERROR
What are your sources of error?
 After you have looked very carefully at your results?
 Are there any factors that could have affected your resuts?
 Here are some possible sources of error:
 Failure to account for a factor (usually systematic)  The most challenging part of designing an experiment is trying to control or account for all possible factors except the one independent variable that is being analyzed. The best way to account for these sources of error is to brainstorm with your peers about all the factors that could possibly affect your result. This brainstorm should be done before beginning the experiment so that arrangements can be made to account for the confounding factors before taking data. Sometimes a correction can be applied to a result after taking data, but this is inefficient and not always possible.
 Environmental factors (systematic or random)  Be aware of errors introduced by your immediate working environment. You may need to take account for or protect your experiment from vibrations, drafts, changes in temperature, electronic noise or other effects from nearby apparatus.
 Instrument resolution (random)  All instruments have finite precision that limits the ability to resolve small measurement differences. One of the best ways to obtain more precise measurements is to use a null difference method instead of measuring a quantity directly. Null or balance methods involve using instrumentation to measure the difference between two similar quantities, one of which is known very accurately and is adjustable. The adjustable reference quantity is varied until the difference is reduced to zero. The two quantities are then balanced and the magnitude of the unknown quantity can be found by comparison with the reference sample. With this method, problems of source instability are eliminated, and the measuring instrument can be very sensitive and does not even need a scale.
 Failure to calibrate or check zero of instrument (systematic)  Whenever possible, the calibration of an instrument should be checked before taking data. If a calibration standard is not available, the accuracy of the instrument should be checked by comparing with another instrument that is at least as precise, or by consulting the technical data provided by the manufacturer. When making a measurement with a micrometer, electronic balance, or an electrical meter, always check the zero reading first. Rezero the instrument if possible, or measure the displacement of the zero reading from the true zero and correct any measurements accordingly. It is a good idea to check the zero reading throughout the experiment.
 Physical variations (random)  It is always wise to obtain multiple measurements over the entire range being investigated. Doing so often reveals variations that might otherwise go undetected. If desired, these variations may be cause for closer examination, or they may be combined to find an average value.
 Parallax (systematic or random)  This error can occur whenever there is some distance between the measuring scale and the indicator used to obtain a measurement. If the observer's eye is not squarely aligned with the pointer and scale, the reading may be too high or low (some analog meters have mirrors to help with this alignment).
 Instrument drift (systematic)  Most electronic instruments have readings that drift over time. The amount of drift is generally not a concern, but occasionally this source of error can be significant and should be considered.
 Lag time and hysteresis (systematic)  Some measuring devices require time to reach equilibrium, and taking a measurement before the instrument is stable will result in a measurement that is generally too low. The most common example is taking temperature readings with a thermometer that has not reached thermal equilibrium with its environment. A similar effect is hysteresis where the instrument readings lag behind and appear to have a "memory" effect as data are taken sequentially moving up or down through a range of values. Hysteresis is most commonly associated with materials that become magnetized when a changing magnetic field is applied.
 There is no such thing as "human error"! This vague phrase does not describe the source of error clearly.
VALIDITY AND RELIABILITY
DISTRICT SCIENCE FAIR ELIGIBILITY
Each stage of this learning guide will advise you of potential roadblocks to your science fair success. Please make sure you understand and read this information carefully. Failure to do so could affect your eligibility to participate in the District Science Fair and science fairs beyond.
 Ensure that your raw data and observations has been recorded in your science log book.
 All participants must group and summarize their raw data using basic statistical analysis techniques.
 Data should be summarized in a table and displayed graphically in order to visualize trends and patterns in the data.
 To compete at the competitive science fair level, your data must be analyzed using more advanced statisitcal analysis techniques.