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Page history last edited by gerryc 11 years, 5 months ago

Practical Skills

Below are a list of skills required of you by the Syllabus. I have some links associated with those skills but not many at this stage. Any suggestions would be gratefully recieved.



Student Outcomes: Hypotheses

State a testable hypothesis.

Here is a slideshow to show you how to create a useful hypothesis.


Design and carry out an investigation to test the hypothesis.

Identify the variables in an experiment, and classify them as independent or dependent.


Want to try a really simple experiment? Go here and see what happens when you change a few variables.


Identify any quantities that are deliberately held constant during an experiment.


Hey! Here is an animation which will test your ability to indentify these variables - go on try it!


Student Outcomes: Procedure

Follow instructions accurately and safely.

Describe and explain the procedure for a given experiment.

Draw or interpret diagrams of the apparatus used in an experiment.


Here is a rubric guide to help identify the important skills in drawing diagrams in biology.

It's quite good - I may even use it sometime!



Level 1

Level 2

Level 3

Level 4


Diagram Quality

·         page in size

·         pencil

·         stippled, not shaded

·         neatness

Demonstrates limited competence in biological drawing skills.

Demonstrates moderate competence in biological drawing skills.

Demonstrates considerable competence in biological drawing skills.

Demonstrates a high degree of competence in biological drawing skills.



·         left-aligned, on right

·         accurate

·         label lines parallel

Demonstrates limited accuracy in labeling.

Demonstrates moderate accuracy in labeling.

Demonstrates considerable accuracy in labeling.

Demonstrates a high degree of accuracy in labeling.

Required Information

·         Title, Subtitle

·         condition of specimen

·         estimated size

·         Field of View (F.O.V.)

Most required information is missing.

Some required information is missing.

Most required information is given.

All required information is given.

Drawing Magnification

·         actual size of specimen

·         drawing size

·         calculations accurate

·         units given

No drawing magnification &/or viewing magnification given.

Demonstrates some accuracy in calculation of drawing magnification.

Demonstrates considerable accuracy in calculation of drawing magnification.

Demonstrates a high degree of accuracy in calculation of drawing magnification.


Source: http://virtuallibrary.stao.ca/sci-tie-data/lessons/biology/b11/11biorubric.html


Negotiate with others the role of each member in a team.


Student Outcomes: Observation

Describe a pattern or behaviour observed in a practical activity.


Distinguish between the terms ‘precision’ and ‘accuracy’.


Accuracy is defined as, "The ability of a measurement to match the actual value of the quantity being measured".

If in reality it is 34.0 C outside and a temperature sensor reads 34.0 C, then than sensor is accurate.


Precision is defined as, "(1) The ability of a measurement to be consistently reproduced" and "(2) The number of significant digits to which a value has been reliably measured".

If on several tests the temperature sensor matches the actual temperature while the actual temperature is held constant, then the temperature sensor is precise. By the second definition, the number 3.1415 is more precise than the number 3.14


Source: http://www.theweatherprediction.com/habyhints/246/


Here is a nice video explaining the difference (finally!):


Here is a nice quiz as well - Slide 1 poses the questions, Slide 2 gives the answers.




Even more information here.


Record data from an experiment to an appropriate degree of precision.


Nice summary here.


Identify the sources of random and systematic errors in an experiment.


Measurement errors fall into two categories: random and systematic.

The random error in an individual measurement is not predictable in exact magnitude or sign.

However, the average of random errors over many repeated, independent measurements of the same quantity is zero.

Thus, the average of several measurements is generally closer to the actual value than any of the individual measurements.


The other type of error is “systematic”. Here the magnitude and sign of the error is always the same.

The measurement result is shifted, up or down, by the same amount, even in repeated measurements.

Thus, averaging many measurements will not improve results. Typically a measurement has both random and systematic errors.





See this SACE Board slide show covering all the major skills you need to know! (NOTE: 8.3 Mb file)



Prefer a hands on demonstration? This is a little long-winded but it covers the main ideas.

Student Outcomes: Presentation

Present data in tabular form with appropriate column headings, including symbols and units.


Go to this page if you don't know the basics of graphs. It will show you how to create the major type of graph we use.


This is a good introduction to graphs/charts etc. in general:


Example table (Note: I made the data up!)


Energy requirements as you get older

Age in Years

Energy Requirements (kJ)


















Plot a graph of a dependent variable versus an independent variable, with appropriate scales and units.


How would you graph the above table?

Most students would make the following errors.


  1. Draw a bar or column chart. I would prefer you draw line graph. Why? Because they are simple. Just plot the points and then it is easy to draw a line of best fit.


  2. Put the data for the independent variable on the y-axis. If you don't know what I am talking about you better find out what these two terms mean - and I mean FAST!


  3. Draw a graph that looks like this:
  4. Just gorgeous! Just useless! How many errors can you spot? It should look more like the one below.


    Even this is tricky because of the limitations of my knowledge of Excel. Notice it has a line of best fit. See part 5 below. My point is that the second graph is far more USEFUL than the first because it displays some subtle changes in energy requirements that the first graph does not show.


  5. Fail to add any of the following: axis labels (including units), titles, legends (where appropriate). Usually people are pretty good at guessing but sometimes (ie always) they need help. Do help.


  6. Fail to add a line of best fit. This is not crucial in every graph but where you are graphing averages across a range, it is vital. (Hey, looky! Check out the next major heading!)


Go here to see a video showing how to intepret and draw line graphs. Check out the Scottish accent!


If you still don't get it, go here to go through the basics.


Here is another site from Statistics Canada showing how to graph - note, very basic.


Don't like those other sites. How about this: Seven Basic Rules for Making Charts and Graphs.



Draw a line or curve of best fit through a series of points on the graph.


A best-fit line shows the relationship between two variables (x and y) and how much one increases or decreases as the other does. The line you draw represents an "average" of all the data points, most of which will probably not lie exactly on the line itself. Once you draw a best-fit line, you can predict a y value if you know the corresponding x value, and vice versa. This ability to predict or estimate values when you don't know what the actual values are for sure is why you draw a best-fit line in the first place.


Source: http://www.er9.org/jbhs/Curriculum/EB/HowToDrawABestFitLine/HowToDrawABestFit.html

(Note: this site also has a technique for drawing a graph and what a line of best fit looks like.  1994 though!)


This video is about negative and positive correlation. If you have no idea what a line of best fit is, this video will baffle you and the jazz music will irritate.

However, if you are willing to persist, it isn't a bad introduction to the idea. The stuff about "best fit" is about 2:30 into the video.


In general if you are investigating a simple relationship between two variables you will be able to draw a line of best fit. A line of best fit shows the pattern or trend you (think) you would get if you were to remove all the random measurement errors from your experiment.


A line of best fit may be straight or curved. It may pass through some of the points but won't pass through all of them. The quickest way to lose marks is to join the points dot-to-dot style; you will immediately drop down to 4 marks!


Another common mistake is to make the line of best fit pass through the origin when it shouldn't. Every experiment is different so you need to work out where the graph will go for small and large values of the independent variable if you are unable to measure them.


If you can't draw neat curves by hand then consider getting one of the special flexible rulers that are designed specifically for this. They make the job much easier to complete. It is always a good idea to mark the line in very faintly in pencil, then to go over it again when you are completely happy with it.


Source: http://www.8886.co.uk/analyse.htm


Further, here is an Illuminations site which lets you plot points are random, guess the line of best fit and then add the actual LOBF for you to compare. Just follow the instructions. Unfortunately, it doesn't do curved lines of best fit ;-(

Student Outcomes: Sample Size

Understand that increasing the sample size in an experiment will reduce the effect of random errors.

Imagine if you were about to buy a new car. You were told that for one model, a Forden, they taken one car out of every 1000 made and do various safety tests.  For another model that sells as well, called the Hoyota, they take 50 cars out of every 1000 made and do the same tests. If both car companies (Forden and Hoyota) say that the cars they tested were found to be 99% safe, whose tests would you consider the most reliable? Why? The chances are higher in the Hoyota factory that any random errors that occured during manufacturing will be picked up with their sampling methods compared to the Forden company. 50 cars is a much larger sample size than one in a thousand.


Student Outcomes: Repetition

Understand that valid results are reproducible.


See the SACE Board Presentation above.


Student Outcomes: Intepretation and Evaluation of an experiment

Use data generated from an experiment to draw conclusions about the purpose of the experiment.

Evaluate an experiment for strengths and weaknesses, and suggest improvements.


Go here for a good summary of how to conduct a Scientific Experiment.


Too much detail? Try here for a simpler version.


This is an excellent video on the Scientific Method by Mr Anderson from Bozeman Science.


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