Observing Tropic Responses: Skills

"The seedlings grew towards the light" and "the seedlings grew at a mean angle of 30 degrees towards the light" are both true. Only one of them can be plotted, compared or tested. This page is about the difference, and about what can go wrong when you try to measure a plant.

What tropisms are

Plant growth is affected by factors in the external environment: light, gravity, water, and the presence of objects. These growth responses are tropisms, and because a plant cannot move, they are its main way of responding to where it finds itself.

Tropisms improve a plant’s chances of survival:

• Growing towards light gives a maximum rate of photosynthesis.
• Growing away from or towards gravity makes sure seedlings come up the right way round.
• Growing towards water lets roots maximise water uptake.
• Growing up and around an object lets a plant gain height quickly and so absorb more light.

Investigating a tropic response

There are lots of ways to set an investigation up, and the IB expects you to have done some of them:

• Grow plants under different light sources using photographic filters — red, blue, green.
• Remove or cover the shoot tip to see whether the response still occurs.
• Place the seedling radicle in different positions — facing up, facing down, horizontal.

Whatever the setup, the data can be collected in two ways: qualitative diagrams of seedling growth, or quantitative measurements of the angle of curvature.

Qualitative and quantitative results

Two types of experiment, producing two kinds of result.

Qualitative

• Observations are recorded without collecting numerical data.
• The starch test with iodine is a qualitative test — you record a colour change.
• Other examples: smells, tastes, textures, sounds, descriptions of the weather or of a habitat.
• They cannot be processed mathematically, but they can be analysed — compared against a standard or against other experimental work.
• They are usually recorded as words, short sentences and descriptions.

Quantitative

• Numerical data is collected and recorded.
• Percentage cover from a quadrat is a quantitative measurement. So are temperature, pH, time, volume, length and mass.
• You need apparatus that measures the quantity you want.
• Results must be processed mathematically before analysis: means and rates, then standard deviation and standard error, then statistical tests such as chi-squared or a t-test.

So the mean of 11, 12 and 14 is recorded as 12 or 12.3. It is not recorded as 12.3333333 — writing more decimal places than your measurements justify is claiming a precision you did not have.

Neither type is automatically better. Which one is more useful depends on what is being observed and what the experiment is for — and often you want both. It could be argued that qualitative results are more subjective, but in fact both types are subject to bias and error, so both need careful tools and systems for recording, and qualitative observations should be kept as objective as possible.

Precision, accuracy and reliability

These three words get used interchangeably in ordinary speech. In an exam they mean three different things, and confusing precision with accuracy is one of the most common errors in the whole course.

• Precision — how close repeated readings are to each other. Measurements to more decimal places are more precise.
• Accuracy — how close measurements are to the true value.

Measurements can be precise but not accurate, if every reading carries the same error.

The two kinds of error

• Random errors cause unpredictable fluctuations in readings because of uncontrollable factors such as environmental conditions. They affect precision, giving a wider spread about the mean. Reduce them by repeating measurements and taking an average, and by using instruments with an appropriate degree of precision.
• Systematic errors arise from faulty instruments or flaws in the method. The same error is repeated every single time, so they affect the accuracy of every reading. Reduce them by recalibrating the instrument, using a different one, or correcting the technique.

Reliability is the consistency of your results. It is increased by measuring carefully and accurately, by repeating trials and averaging so that outliers matter less, and because repeating also lets you see random errors and anomalies so they can be disregarded.

Worked examples