Biology Paper 2 - 1 Practical Skills

Closeness of data to the known or true value. Accuracy can be improved by collecting more data, across a smaller independent variable range and using smaller/closer increments; e.g. to find the optimum value of an enzyme catalysed reaction you may collect between 35-40°C at intervals of 2°C.

A data value, outlier, that lies beyond the expected variation. Be aware that a considerable range of data is required in order to identify anomalies; in a repeat of three data sets, one data point that does not fit the perceived trend cannot be termed an anomaly. Multiple data sets are required to either highlight the outlier or remove it; as the data set becomes larger the impact of the outlier on the mean is reduced.

A qualitative judgement on the extent to which the a conclusion can be drawn from the data. If there is a strong correlation between data and the conclusion then confidence is also strong and vice versa.

The difference between a measurement and its true value. Human error is never an accepted answer! You need to be specific in your answer; eg when reading from a biuret your eyes should be level with the liquid being read to avoid parallax.

Closeness of values within a data set. The closer the values, the more precise the data. It is also a measure of the number of decimal places used to record quantitative data. Precision is improved measuring to more decimal places and repeating the data sets to identify and remove anomalies.

The precision of measurements achieved by one person in a short length of time, using the same method and equipment. Note this should not be referred to as reliable!

The precision of measurements achieved by different people, over a different length of time, using the same method and comparable equipment under comparable conditions.

The smallest change that can be detected by the instrument being used to measure; eg when using a microscope, resolution is the ability to distinguish between two points.

An estimate attached to a measurement which characterises the range of values within which the true value is asserted to lie. This is normally expressed as a range of values such as 44.0 ± 0.4. Uncertainty can be reduced by choosing appropriate equipment; eg using a 10ml measuring cylinder to measure 8ml of water, rather than a 100ml measuring cylinder.

A measurement is valid if it measures what it is supposed to be measuring. If an investigation is valid another group can use the same method, collect similar results and draw the same conclusion. Validity will be reduced if no negative control is included in an investigation.

percentage uncertainty = uncertainty divided by quantity measured x100%

All raw data in a single table with ruled lines and border.
Independent variable (IV) in the first column; dependent variable (DV) in columns to the right (for quantitative observations) OR descriptive comments in columns to the right (for qualitative observations).
Processed data (e.g. means, rates, standard deviations) in columns to the far right.
No calculations in the table, only calculated values.
Each column headed with informative description (for qualitative data) or physical quantity and correct units (for quantitative data).
Units separated from physical quantity using either brackets or a solidus (/).
No units in the body of the table, only in the column headings.
Raw data recorded to a number of decimal places appropriate to the least accurate piece of equipment used to measure it.
All raw data of the same type recorded to the same number of decimal places.
Processed data recorded to up to one decimal place more than the raw data.
Remember trailing zeros.