Observing Tropic Responses
- Plant growth can be affected by factors in the external environment such as light, gravity, water, and the presence of objects
- These growth responses are known as tropisms
- Tropisms can be towards a stimulus; positive tropisms, or away from a stimulus; negative tropisms
- Tropisms enable plants to maximise their chances of survival e.g.
- Growing towards light ensures a maximum rate of photosynthesis
- Growing away from or towards gravity ensures that seedlings grow the right way up
- Growing towards water enables roots to maximise their water uptake
- Growing up and around an object may allow a plant to gain height quickly and so maximise light absorption for photosynthesis
- Tropisms are regulated by chemicals produced in plants known as plant hormones
- Examples of tropisms include
- Phototropism
- Plant response to light
- Plant stems grow towards light; this is positive phototropism
- Gravitropism
- Plant response to gravity
- Plant stems grow away from gravity; this is negative gravitropism
- Plant roots grow towards gravity; this is positive gravitropism
- Gravitropism is also known as geotropism
- Phototropism
- Tropisms can be investigated and the responses to a variety of stimuli can be observed or measured
- Data collected can either be through
- Qualitative diagrams of seedling growth
- Quantitative measurements of the angle of curvature of seedlings
- There are many ways to investigate tropic responses of seedlings
- Plants may be grown in varying light sources using photographic filters e.g. red, blue green
- Shoot tips can be removed or covered up
- Seedling radicle may be placed in varying positions e.g. facing up, facing down, facing horizontally
Examiner Tip
You should have had the opportunity to gather data on tropic responses in seedling growth as part of your learning of this course.
NOS: Students should be able to distinguish between qualitative and quantitative observations and understand factors that limit the precision of measurements and their accuracy.
- There are two types of experiment, which in turn obtain two kinds of results:
- Qualitative experiments are used to obtain qualitative results
- Observations are recorded without collecting numerical data
- For example, the starch test using iodine is a qualitative test - a colour change is recorded
- Other common qualitative measurements include smells, tastes, textures, sounds and descriptions of the weather or of a particular habitat
- Qualitative results can't be processed mathematically (there isn't any numerical data) but the observations can be analysed
- The observations may be compared to a standard or other experimental work
- Qualitative results are most often recorded in the form of words, short sentences and descriptions, such as describing a colour change, making a note of someone's opinion, describing the appearance or behaviour of an organism, or describing a chemical reaction
- Quantitative experiments are used to obtain quantitative results
- Numerical data is collected and recorded
- For example, recording the percentage cover of a plant species using a quadrat - a numerical value (a percentage) is recorded
- Other common quantitative measurements include temperature, pH, time, volume, length and mass
- In order to collect numerical data, a quantitative experiment must use apparatus that measures or collects this type of data
- Quantitative results must be processed using mathematical skills prior to analysis
- Simple calculations work out means and rates
- Further calculations are done to obtain information surrounding means (standard deviation and standard error)
- Statistical tests are performed to better understand the results (chi-squared and t-test etc.)
- Quantitative results must all be recorded to the same number of decimal places but processed data can be recorded to the same number of decimal places or to one more decimal place than the raw data
- For example, the mean of 11, 12 and 14 can be recorded as 12 or 12.3 but not 12.3333333
Reaching valid conclusions from qualitative and quantitative results
- It could be argued that qualitative results can be more subjective (i.e. influenced by the person making the observations), but in fact, both types of results are subject to bias and error
- Tools and systems for data gathering and recording are important for both
- Care should be taken when making qualitative observations to keep them as objective as possible (i.e. not allowing observations to be influenced by the person making them)
- In terms of scientific research (and especially in biological experiments sometimes), one type of results is not necessarily better than the other
- The value of qualitative and quantitative data depends on the thing being observed and the purpose of the experiment
- Sometimes it’s important and very useful to use both
- In the example table below, both qualitative and quantitative observations have been recorded whilst observing tropic responses of seedlings and both sets of observations can be useful in drawing conclusions (although as always, the validity of any conclusions drawn can be increased by repeating the experiments and gathering more data)
| Qualitative observations | Quantitative observations |
| Seedlings have grown toward the light | 10 seedlings have grown at a 30o angle toward the light |
| Seedlings in green light have not grown as well as seedlings in blue light | Seedlings in green light have grown by an average of 0.5cm |
| Seedlings in the dark did not grow and were long and tangled | In the dark seedling length reached an average of 15.6cm |
Precision and Accuracy
- The certainty of any conclusions made from an experiment are impacted by the precision and accuracy of measurements and data
- It is a very common mistake to confuse precision with accuracy – measurements can be precise but not accurate if each measurement reading has the same error
- Precision refers to the ability to take multiple readings with an instrument that are close to each other, whereas accuracy is the closeness of those measurements to the true value
Increasing Precision
- Precise measurements are ones in which there is very little spread about the mean value, in other words, how close the measured values are to each other
- If a measurement is repeated several times, it can be described as precise when the values are very similar to, or the same as, each other
- The precision of a measurement is reflected in the values recorded – measurements to a greater number of decimal places are said to be more precise than those to a whole number
- Random errors cause unpredictable fluctuations in an instrument’s readings as a result of uncontrollable factors, such as environmental conditions
- This affects the precision of the measurements taken, causing a wider spread of results about the mean value
- To reduce random error:
- Repeat measurements several times and calculate an average from them
Increasing Accuracy
- A measurement is considered accurate if it is close to the true value
- Systematic errors arise from the use of faulty instruments used or from flaws in the experimental method
- This type of error is repeated consistently every time the instrument is used or the method is followed, which affects the accuracy of all readings obtained
- To reduce systematic errors:
- Instruments should be recalibrated, or different instruments should be used
- Corrections or adjustments should be made to the technique
Increasing Reliability
- The reliability of an experiment can be described as the consistency of the results
- Reliability of an experiment can be increased by taking measurements carefully and accurately
- Using measuring instruments with the appropriate degree of precision can reduce random errors
- Performing several trials and calculating an average of the data means the effect of outliers will be reduced. Repeating trials also allows you to see random errors and anomalies that can be disregarded
