### Presenting Experimental Data

- There are many different types of experiments that can be conducted in biology
- The data collected from biological experiments can vary greatly across the subject
- For example, the large amounts of numerical data produced from ecological studies is very different to the drawings produced from microscope slides of live specimens

- The nature of an experiment dictates how the data should be presented
- It is important that scientists can make the correct judgment when deciding how to present data from an experiment

#### Collecting data

**Qualitative**experiments involve collecting and recording**observations****Quantitative**experiments involve collecting and recording**numerical data**- Recording experimental data in a table is important for any type of experiment
- The table used will vary considerably depending on the specific requirements

- When constructing such a table:
- Draw lines with a ruler to separate cells
- Use appropriate headings
- Use the correct units and symbols (in the headings, not the cells)
- The independent variable should be in the first column
- Any dependent variable readings should be in the subsequent columns

**Examples of a table that has been correctly constructed for an experiment**

#### Processing data

- Depending on the type of experiment, data is processed using different methods (before being analysed)
- Some data does not require any processing, like drawings from life

- 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

- 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.)

- In addition to these mathematical calculations, the data can be presented in
**graphical form**- Graphs, bar charts, and histograms can be used to display quantitative data
- The type of graphical format used depends on the data
- For qualitative and discrete data,
**bar charts**or**pie charts**are most suitable - For continuous data,
**line graphs**or**scatter graphs**are most suitable

- For qualitative and discrete data,

- Any graph drawn should have:
- The appropriate scale with equal intervals
- Labelled axes with the correct units
- Straight lines drawn with a ruler

**The line graph has been used to display continuous data over time while the bar chart has been used to display grouped data**

### Precision & 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

#### 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

#### 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

- Instruments should be

*The difference between precise and accurate results*

#### Uncertainty

- Measurements of quantities are made with the aim of finding the true value of that quantity
- In reality, it is impossible to obtain the true value of any quantity as there will always be a degree of uncertainty
**Uncertainty**is the amount of**error**your measurements might contain- Results from experiments always contain some error (they are never perfect)
- There will always be a small degree of uncertainty in your readings or measurements
- This is often because the
**accuracy and precision**of the apparatus being used is**limited**

- This is often because the
- The
**margins of error**of the apparatus are usually displayed on the glassware - These margins of error can be used to calculate percentage error
- Percentage error helps to quantify the margin of error and its possible
**impact on the results**

- Percentage error helps to quantify the margin of error and its possible
- For example, you may want to measure a
**reaction rate**by measuring how much of a**product**is**made**in a**given time period**(e.g. using a gas syringe to measure the volume of oxygen produced from the breakdown of hydrogen peroxide by catalase)- The gas syringe may only give readings to the nearest
**1 cm**^{3} - The gas syringe has a margin of error of
**± 0.05 cm**^{3} - A ‘±’ sign tells you the
**range**in which the true value lies - The real volume produced could be up to 0.05 cm
^{3}**smaller**or**larger**

- The gas syringe may only give readings to the nearest
- For experiments, you may need to
**calculate**the**percentage error**of your measurements- As long as you know the
**uncertainty value**of your measurements, the percentage error can be calculated using the following formula:

- As long as you know the

**percentage error = (uncertainty value ÷ your measurement) x 100**

- A percentage error less than 5% is considered statistically not significant

#### Choosing the apparatus with the right resolution

- Resolution is the smallest change in the quantity being measured of a measuring instrument that gives a perceptible change in the reading
- For example, the resolution of a wristwatch is 1 s, whereas the resolution of a digital stop-clock is typically 10 ms (0.01 s)
- In imaging, resolution can also be described as the ability to see two structures as two separate structures rather than as one fuzzy entity
- When choosing measuring instruments, instruments with an appropriate measuring scale need to be used
- Smaller measuring instruments have
**higher resolution scales**due to the**smaller graduations**on the scale. This means they have**smaller margins of error** - For example, measuring 5 cm
^{3}of a liquid using a 500 cm^{3}measuring cylinder would be very difficult. A 10cm^{3 }measuring cylinder would be a more appropriate choice as the measuring scale is of a higher resolution

**Smaller measuring instruments tend to have higher resolution measurements and a smaller margin of error. Make sure to always choose the appropriate instrument for the experiment**

#### Worked Example

In an enzyme rate reaction involving the breakdown of hydrogen peroxide by catalase, 50 cm^{3} of oxygen was produced, with an uncertainty value of 0.05 cm^{3}. Calculate the percentage error of this measurement.

Percentage error = (uncertainty value ÷ your measurement) x 100

Percentage error = (0.05 ÷ 50) x 100

Percentage error = 0.001 x 100

**Percentage error = 0.1%**

#### Worked Example

In an enzyme rate experiment involving the breakdown of hydrogen peroxide by catalase, a student recorded that 10 cm^{3} of oxygen was produced in 5.245 seconds. The student measured this using a stopwatch that counted in milliseconds. Calculate the percentage error of the stopwatch measurements.

**Step 1: Calculate the uncertainty value**

The stopwatch can measure to the nearest millisecond (**0.001** seconds)

This means the actual time taken could be up to **0.0005** seconds **shorter** or **longer** than this

This means stopwatch measurements have an uncertainty value of** ± 0.0005 s**

**Step 2: Calculate the percentage error **of the student’s measurement of 5.245 seconds

Percentage error = (uncertainty value ÷ your measurement) x 100

Percentage error = (0.0005 ÷ 5.245) x 100

Percentage error = 0.000095 x 100

**Percentage error = 0.0095% or 0.01%**

### Qualitative and Quantitative Results

- 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

**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

#### Recording qualitative and quantitative results

- 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 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 a field of butterflies 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**)

- The value of qualitative and quantitative data depends on the thing being observed and the

**Qualitative and Quantitative Observations Table**