Experimental Design

Independent Variable

Variable that is manipulated or changed by the experimenter.

Dependent Variable

Variable that is affected and  measured.

Controlled Variables

Variable/s that remain the same during an experiment.

Ensures that changes occurring are due to the IV only.

Extraneous Variables

A factor that is not kept constant or accounted for in an experiment is known as an uncontrolled or extraneous variable.

Formulate a Hypothesis

A hypothesis should:

•Be a testable statement

•Describe how you think the IV will affect the DV, including the direction of change (increase/decrease)

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Template

If [the IV] then [the DV] because [scientific reasoning]. 

Experimental Group

Are groups/samples in which the IV is manipulated.

Also known as the treatment group.

There may be different levels to experimental groups. 

Control Group

A group/sample that is being tested with no changes (no IV).

Also known as the experimental control.

Used as a comparison with the experimental groups to determine the effect of changing the IV.

Placebos

A placebo is a substance or treatment that appears real but has no therapeutic effect.

It is used in experiments to compare the effects of a real treatment against a control group receiving the placebo.

The placebo effect refers to the psychological or physical response a person experiences after being given a placebo, which can include feeling better or experiencing specific symptoms.

E.g. sugar pill or saline injections

Validity

A valid experiment is when only one variable is changed at a time (IV) and all other variables are controlled.

Note: a variable is any factor that can be altered or controlled in an experiment.

To be valid: change only one IV at a time, include a control group, control more variables.

Quantitative Data

 is numerical data and objective.

E.g. time, temperature, mass

For a test to be reliable, it must produce quantitative results (data);

i.e. something must be measured.

Qualitative Data

data is descriptive and subjective.

E.g. colour change

Primary Data

data is original data collected by a researcher,

Secondary Data

is data that already exists and was collected by someone else.

Reliability

Describes an experiment that produces the same results when repeated – the results can be trusted.

To be reliable:

•Quantitative data collection

•Repeated 5+ times to calculate an average

Repeatability

An experiment is repeatable if other can produce similar results under the same lab conditions.

The closeness of the agreement between the results of successive measurements of the same quantity being measured, carried out under the same conditions of measurement. These conditions include:

•The same measurement procedure

•The same observer

•The same measuring instrument used under the same conditions

•The same location

•Repetition over a short period of time

Reproducibility

An experiment is reproducible if others can produce similar results under different lab conditions

The closeness of the agreement between the results of measurements of the same quantity being measured, carried out under changed conditions of measurement. These different conditions include:

•A different method of measurement

•A different observer

•A different measuring instrument

•Different location

•Different conditions of use

•Different time

Repeatability & Reproducibility

The purposes of reproducing experiments include checking of claimed precision and identifying any systematic errors from one or other experiments/groups that may affect accuracy.

Experiments that use subjective human judgement/s, involve small sample sizes or insufficient trials may also yield results that may not be repeatable and/or reproducible.

Accuracy

the accuracy of a measurement refers to how close it is to the ‘true’ value of the quantity being measured.

The true value is the value, or range of values, that would be found if the quantity could be measured perfectly.

A measurement is considered accurate if it is judged to be close to the true value of the quantity being measured.

Precision

Refers to how closely a set of measurement values agree with each other.

Precision gives no indication of how close the measurements are to the true value and is therefore a separate consideration to accuracy.

A set of precise measurements will have very little spread about their mean value.

Types of Errors

• Personal

• Random

• Systematic

Personal Errors

VCAA: Personal errors include mistakes or miscalculations.

E.g. measuring a height when the depth should have been measured,  misreading the scale on a thermometer as 35^oC rather than 25^oC or using the diameter instead of the radius when calculating the area of a circle using the formula A = π r².

Personal errors should be eliminated by performing the experiment again correctly the next time, and therefore do not form part of an analysis of data quality.

Random Errors

VCAA: Random errors affect the precision of a measurement and are present in all measurements except for measurements involving counting.

They are unpredictable variations in the measurement process and result in a spread of readings.

The effect of random errors can be reduced by making more or repeated measurements and calculating a new mean (average) and/or by refining the measurement method or technique.

Systematic Errors

VCAA: Systematic errors affect the accuracy of a measurement.

Systematic errors cause readings to differ from the true value by a consistent amount each time a measurement is made, so that all the readings are shifted in one direction from the true value.

The accuracy of measurements subject to systematic errors cannot be improved by repeating those measurements.

Reduce Errors by

•Selecting appropriate equipment

•Calibrating equipment properly

•Using equipment correctly

•Using a larger sample size

•Taking repeat measurements and calculating the average

Scientific Method

•IV identified and systematically changed

•Control group explicitly identified

•Control all other variables; include 3 most relevant examples

•DV is measured quantitatively; how and what data will be recorded?

•Repeated 5+ times and average DV calculated

•Statement of expected results if Hypothesis is supported

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ICCDRH:

Independent variable, control group, controlled variables, dependent variable, reliability, hypothesis

Describing Graphs

1.Statement of relationship:

e.g. As the (IV) increases, the (DV) _____________ in a ___________ trend (describe the pattern).

2.Use mathematical terms:

Linear, exponential, positive, negative, plateau, peak, constant, etc.

3.Refer to a specific numerical value from the graph:

e.g. When the (IV) is ___________, the (DV) is ___________, and this increases to ___________, when the IV increases to _______________.

Conclusion

•Why the hypothesis was supported/refuted

•Summary of limitations and improvements

•Implications for further research, impact on scientific knowledge or impact on society/environment