Practical Skills: Written Assessment (OCR A Level Biology): Flashcards

False — preliminary research is carried out before designing the experiment ('preliminary' means 'to come before').

True — other variables affecting the results will not have been identified and controlled.

Any factor in an experiment that could change or be changed.

The only variable that should be changed throughout an experiment.

The variable that is measured to determine the outcome (results) of an experiment.

Any other variables that may affect the results of the experiment and so need to be controlled or monitored.

So that the results of the experiment are valid.

They can be used to:\n\n- Identify the variables within an experiment\n\n- Determine effective ways of controlling those variables

It is likely to contain information about the different factors or variables that may exist and could affect the investigation.

To determine the effect of changing a particular factor on the rate of a reaction that is catalysed by an enzyme.

• Temperature\n\n- pH\n\n- Enzyme concentration\n\n- Substrate concentration

Only one variable should be changed (the independent variable); all other variables must be controlled and kept the same.

• pH\n\n- Enzyme concentration\n\n- Substrate concentration

They could affect the results, making the results unreliable.

The independent variable is the only variable that should be changed throughout an experiment.

The variable that is measured to determine the outcome of an experiment is the dependent variable.

False — only one variable (the independent variable) should be changed at a time.

True

Repeat the experiment yourself, following the instructions provided, and determine whether you can produce similar results.

• Method limitations

• Accuracy

• Precision

• Reliability

• Validity

Any experimental design flaw or fault in the method that affects the accuracy of the results.

An air bubble inside the plant xylem prevents the accurate measurement of water uptake.

How close a reading or measurement is to its true value.

How similar repeat readings or measurements are to each other; tightly clustered readings (a small range) are described as precise.

A systematic error is produced by faulty instruments or flaws in the method and is repeated consistently every time.

A random error is caused by unexpected environmental changes or incorrect use of equipment and is different every time (e.g. a breeze during a potometer experiment).

Measurements recorded to a greater number of decimal places are more precise than those recorded to a whole number.

Experiments are repeated many times to ensure the reliability of results.

The other variables are identified and controlled so that only the effect of the independent variable is being tested.

Yes — measurements can be precise (close to each other) but not accurate if each reading has the same error, so they cluster together yet lie far from the true value.

At the preliminary stage, so that any corrections or adjustments can be made before conducting the actual experiment.

Scientists always record instructions for their experiments so that they can be repeated by another individual without any additional help.

• The apparatus used

• The quantities of specific reactants/reagents used

• The species of model organism used

Ensure all plant stems are cut underwater to prevent the entry of air.

False — measurements can be precise (close to each other) but still inaccurate if each reading shares the same error, so they lie far from the true value.

True

Correct units and symbols ensure measurements are communicated accurately and unambiguously, so quantitative data can be compared reliably.\n\nThe correct symbol must always be used with the unit of measurement (e.g. m³ for cubic metres).

1 cm³ is the same as 1 millilitre (ml).

1 dm³ is the same as 1 litre (l).

"Amount" has a specific scientific meaning: the number of moles.\n\nInstead, refer to the mass, volume or concentration of a substance to be precise.

The digits in a number that are reliable and necessary to indicate the quantity of that number.

Yes — all non-zero digits are significant.\n\nFor example, 4107 has 4 significant figures.

No — leading zeros are not significant.\n\nFor example, 0.00079 has 2 significant figures.

No — trailing zeros in a number without a decimal point are not significant.\n\nFor example, 57,000 has 2 significant figures.

Yes — trailing zeros in a number containing a decimal point are significant.\n\nFor example, 689.0023 has 7 significant figures.

When rounding, the digit immediately after the last significant figure you are keeping is called the rounding decider; if it is 5 or greater, you increase the previous value by 1.

• Identify the significant figures in the number using the significant figure rules\n- Count from the first significant figure to the specified number of significant figures\n- Use the next digit as the rounding decider\n- If the decider is 5 or greater, increase the previous value by 1 (otherwise leave it unchanged)

1.05\n\nThe first 3 significant figures are 1.04; the rounding decider is 7 (5 or greater), so the 4 rounds up to 5.

Significant figures must be used whenever you are dealing with quantitative data.

True

False — zeros between non-zero digits are significant (e.g. 29.009 has 5 significant figures).

Observations recorded without collecting numerical data, e.g. a colour change in the iodine starch test, or descriptions of smell, texture or behaviour.

Numerical data that is collected and recorded, e.g. temperature, pH, time, volume, length or mass.

• Lines drawn with a ruler to separate cells\n\n- Appropriate headings\n\n- Correct units and symbols placed in the headings, not the cells\n\n- Independent variable in the first column\n\n- Dependent variable readings in the subsequent columns

Bar charts or pie charts.

• An appropriate scale with equal intervals\n\n- Labelled axes with the correct units\n\n- Straight lines drawn with a ruler

The ability to take multiple readings with an instrument that are close to each other, i.e. there is very little spread about the mean value. Measurements to a greater number of decimal places are more precise.

The closeness of a measurement to the true value.

Random errors cause unpredictable fluctuations in an instrument's readings due to uncontrollable factors, such as environmental conditions.\n\nThey affect the precision of measurements, causing a wider spread of results about the mean value.

Systematic errors arise from faulty instruments or flaws in the experimental method, and are repeated consistently every time.\n\nThey affect the accuracy of all readings obtained.

The amount of error your measurements might contain. It often arises because the accuracy and precision of the apparatus is limited; the margin of error (shown as '±') gives the range in which the true value lies.

percentage error = (uncertainty value ÷ your measurement) × 100\n\nA percentage error of less than 5% is considered statistically not significant.

The smallest change in the quantity being measured that gives a perceptible change in the reading of an instrument. Smaller instruments have higher resolution scales (smaller graduations) and therefore smaller margins of error.

Raw quantitative results must all be recorded to the same number of decimal places.\n\nProcessed data can be recorded to the same number of decimal places, or to one more decimal place than the raw data.\n\nFor example, the mean of 11, 12 and 14 can be recorded as 12 or 12.3, but not 12.3333333.

When making qualitative observations, care should be taken to keep them as objective as possible, i.e. not influenced by the person making them.

Line graphs or scatter graphs.

Repeat measurements several times and calculate an average.

Recalibrate or use different instruments, and make corrections or adjustments to the technique.

True

False — recalibrating reduces systematic errors; random errors are reduced by repeating measurements and averaging.

The average value of a data set.

A measure of the spread or dispersion of data around the mean.

The results lie close to the mean, i.e. there is less variation in the data.

The results are more spread out from the mean, i.e. there is greater variation in the data.

Two data sets can have the same mean but very different spreads of values.

For example, one group's values may lie close to the mean while another group's values lie far from it, so the mean alone hides this difference.

The standard deviation should be used in conjunction with the mean.

It shows the spread of the data, giving a more meaningful comparison than the mean on its own.

The two sets of results are not significantly different.

The two sets of results are significantly different.

On a graph, standard deviation can be represented by standard deviation error bars.

• Qualitative: non-numerical data (e.g. blood group)

• Discrete: numerical data that can only take certain values in a range (e.g. shoe size)

• Continuous: numerical data that can take any value in a range (e.g. height or weight)

Bar charts or pie charts.

• Independent variable on the x-axis

• Dependent variable on the y-axis

• It may be straight or curved depending on the trend shown by the data

• If curved, it should be drawn smoothly

• It should have a balance of data points above and below the line

• It should pass through the origin only if the data and trend allow it

A straight line drawn so that it just touches the curve at a single point, with a slope that matches the slope of the curve at that point.

Many enzyme rate experiments produce curved (non-linear) graphs with an ever-changing gradient, because the reaction rate changes over time.

A tangent is drawn to the curve at a single point and its gradient is calculated to give the rate at that point.

Gradient = change in y-axis ÷ change in x-axis ('rise over run').

Line graphs or scatter graphs.

The initial rate is found from a tangent drawn at the start of the reaction (time = 0).

True — this is why using the mean alone to compare groups can be misleading.

False — continuous data is best shown on a line graph or scatter graph; bar and pie charts suit qualitative and discrete data.

Conclusions can only be drawn once the results have been properly evaluated.

The potential impact of any limitations on the data must first be considered, so that any conclusion is more likely to be reliable.

They are two different skills:

• Evaluating the results looks at the data collected and how much confidence can be placed in it.

• Evaluating the procedure looks at the method used to obtain those results.

• If the impact of the limitations is judged to be negligible, a conclusion can more likely be drawn from the results.

• If the limitations could have had a significant impact, it is much harder to draw a conclusion, and any conclusion drawn has a greater chance of being incorrect.

How close measured values are to each other. If repeated measurements are very similar to, or the same as, one another, they can be described as precise.

How close a measured value is to the true value. Accuracy can be increased by repeating measurements and finding a mean average.

Measurements recorded to a greater number of decimal places are more precise than those recorded to a whole number.

Accuracy can be increased by repeating measurements and finding a mean average.

Most apparatus has a margin of error that can be used in percentage error calculations.

The percentage error gives an idea of the magnitude of any error, and therefore how much impact it may have had on the results.

Any conclusions drawn may be rejected, or further testing may be required.

Improvements can be made to the apparatus used or to the experimental procedure in order to reduce the percentage error.

A result that does not fit with the trend or with other replicates in an experiment. It is produced by experimental errors (operator or 'one-off' errors) and typically differs from the mean by more than 10%.

By looking for results, or data points on a graph, that:

• do not fit with the overall trend, or

• do not fit with other replicates carried out during the experiment.

Anomalous results show a larger difference from the mean than the rest of the results.

A result is often taken to be anomalous if it differs from the mean result by more than 10%.

Repeating the experiment several times and removing anomalies makes the data more reliable.

This, in turn, allows more valid conclusions to be drawn.

False — they are two different skills; evaluating results looks at the data collected, while evaluating the procedure looks at the method used to obtain it.

True