I85PREP
⌘ K
QCAA Agricultural Science EA study materials
QCAA Agricultural Science EA study resources

QCAA Agricultural Science EA practice questions

One question at a time. Build your QCAA Agricultural Science EA understanding.

First 50 of 200 questions. Each one lists the choices, the correct answer, and an explanation. The checker at the top of this page saves progress in this browser when it is available.

Continue in the practice tool above to load more questions, 50 at a time.

Question 1Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

In a plant trial, researchers measured stem height in centimetres for 20 seedlings. Which type of data is this?

  1. Discrete quantitative data
  2. Nominal categorical data
  3. Ordinal categorical data
  4. Continuous quantitative data Correct answer.

Explanation

Height is measured on a continuous scale, so any value within a range is possible and the data are continuous quantitative. Discrete data can only take whole-number counts, and categorical data describe groups rather than measured magnitudes.

Question 2Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student wants the most appropriate graph to display the frequency distribution of a continuous variable such as grain weight. Which display is best?

  1. Histogram Correct answer.
  2. Bar chart
  3. Pie chart
  4. Scatterplot

Explanation

A histogram groups continuous measurements into class intervals and shows the frequency in each interval, making it ideal for continuous distributions. Bar charts suit categorical counts, pie charts suit proportions of a whole, and scatterplots show relationships between two variables.

Question 3Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A trial compares mean yield across three fertiliser treatments. Which statistical test is most appropriate for comparing the three means?

  1. Chi-square test
  2. Correlation coefficient
  3. Analysis of variance Correct answer.
  4. Paired t-test

Explanation

Analysis of variance compares the means of three or more groups in a single test, controlling the overall error rate. A t-test is limited to two groups, chi-square deals with frequencies, and correlation measures association between two variables.

Question 4Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A researcher states a null hypothesis before analysing data. What does a null hypothesis typically assert?

  1. That there is no significant difference between treatments Correct answer.
  2. That the sample size is adequate
  3. That the experimental design is flawed
  4. That there is a significant difference between treatments

Explanation

The null hypothesis assumes no real difference or relationship exists between the groups being compared. Statistical tests then determine whether the observed data provide enough evidence to reject this assumption in favour of the alternative hypothesis.

Question 5Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

In an experiment, a p-value of 0.03 is obtained using a significance level of 0.05. What is the correct interpretation?

  1. The null hypothesis is accepted because 0.03 is close to 0.05
  2. The null hypothesis is rejected because 0.03 is less than 0.05 Correct answer.
  3. The alternative hypothesis is rejected because 0.03 is small
  4. The result is inconclusive and requires a larger sample

Explanation

When the p-value is below the chosen significance level, the result is statistically significant and the null hypothesis is rejected. A p-value of 0.03 falls below 0.05, so the difference is unlikely to be due to chance alone.

Question 6Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

Which statement best describes the purpose of a control group in an agricultural experiment?

  1. To randomise the allocation of treatments
  2. To increase the number of replicates
  3. To provide a baseline for comparison without the treatment Correct answer.
  4. To ensure the results are statistically significant

Explanation

A control group is not exposed to the experimental treatment and provides a baseline against which treated groups are compared. This allows any observed effect to be attributed to the treatment rather than other factors.

Question 7Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student repeats each treatment five times in a field trial. What is the main purpose of this replication?

  1. To eliminate the need for a control group
  2. To guarantee a statistically significant result
  3. To reduce the total amount of data collected
  4. To reduce the influence of random variation on results Correct answer.

Explanation

Replication increases the reliability of results by averaging out random environmental and biological variation across multiple experimental units. It does not guarantee significance but makes estimates of treatment effects more precise.

Question 8Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

Why is random allocation of treatments important in a field experiment?

  1. It removes the need to measure variables
  2. It reduces bias from systematic differences among plots Correct answer.
  3. It ensures every plant receives the same treatment
  4. It guarantees the soil is uniform

Explanation

Random allocation distributes treatments across plots by chance so that systematic differences such as soil variation do not consistently favour one treatment. This reduces bias and supports valid comparisons between treatments.

Question 9Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student measures plant height but consistently reads the ruler from an angle, giving values slightly too high. What type of error is this?

  1. Systematic error Correct answer.
  2. Human replication error
  3. Sampling error
  4. Random error

Explanation

A consistent bias in one direction, such as reading a scale from an angle, produces systematic error that shifts all measurements away from the true value. Random error, by contrast, scatters measurements unpredictably above and below the true value.

Question 10Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

Which practice would most improve the accuracy of a measurement of soil pH?

  1. Calibrating the pH meter with buffer solutions before use Correct answer.
  2. Recording values to many decimal places
  3. Using a different meter for each sample
  4. Taking only one reading per sample

Explanation

Calibrating an instrument against known standards corrects systematic bias so readings reflect true values, improving accuracy. Recording extra decimal places or using multiple meters does not correct an underlying calibration error.

Question 11Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A dataset of cattle weights has one value recorded as 8500 kg when all others are near 500 kg. What is this value best described as?

  1. A replicate
  2. An outlier Correct answer.
  3. A median value
  4. A mean value

Explanation

An outlier is a value that lies far outside the general pattern of the rest of the data, often due to recording or measurement error. Such values should be investigated before deciding whether to exclude them from analysis.

Question 12Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A dataset contains the values 4, 6, 6, 8, 11. What is the median?

  1. 11
  2. 8
  3. 6 Correct answer.
  4. 7

Explanation

The median is the middle value when data are ordered by size. With five ordered values, the third value, 6, is the median, and it is unaffected by the extreme value at either end.

Question 13Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

Why is the median often preferred over the mean when a dataset contains extreme outliers?

  1. The median is always larger than the mean
  2. The median is unaffected by extreme values Correct answer.
  3. The median measures spread rather than centre
  4. The median uses every value in its calculation

Explanation

The median depends only on the position of the middle value, so extreme observations do not distort it. The mean incorporates every value, so a single outlier can shift it substantially away from the typical value.

Question 14Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

Two fertiliser treatments produce means of 50 and 55 units with standard deviations of 2 and 12 respectively. What can be concluded?

  1. Both treatments are equally consistent
  2. Consistency cannot be compared from these values
  3. The first treatment is more consistent Correct answer.
  4. The second treatment is more consistent

Explanation

Standard deviation measures the spread of values around the mean, so a smaller standard deviation indicates more consistent results. The first treatment has a much smaller standard deviation, so its yields are more tightly clustered.

Question 15Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

In a line transect survey of pasture, quadrats are placed at regular intervals. What is the main purpose of using quadrats?

  1. To measure soil moisture at depth
  2. To identify the species of grazing animals
  3. To sample a defined area for estimating population density Correct answer.
  4. To record the age of individual plants

Explanation

Quadrats enclose a known area so that counts or cover estimates within them can be scaled up to estimate density or abundance across a larger area. They provide a standardised, repeatable sampling unit.

Question 16Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student samples plants only along the shaded edge of a paddock and generalises to the whole paddock. Which sampling problem does this illustrate?

  1. Random sampling
  2. Stratified sampling
  3. Bias in sampling Correct answer.
  4. Replication

Explanation

Sampling only one edge creates bias because the sample does not represent the whole paddock, which may vary in light, moisture and soil. A representative sample should be collected across the full area or by a defined random or stratified method.

Question 17Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

Which factor is an abiotic component of a pasture ecosystem?

  1. Earthworm population
  2. Soil nitrogen concentration Correct answer.
  3. Grazing cattle
  4. Fungal hyphae in roots

Explanation

Abiotic factors are the non-living physical and chemical components of an ecosystem, such as soil nutrients, temperature, light and moisture. Living organisms, including worms, fungi and cattle, are biotic components.

Question 18Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

In a food chain, what is the role of a decomposer such as soil bacteria?

  1. It converts sunlight into chemical energy
  2. It fixes atmospheric nitrogen only in legume roots
  3. It consumes primary producers directly
  4. It breaks down dead organic matter and releases nutrients Correct answer.

Explanation

Decomposers break down dead organisms and waste, releasing mineral nutrients back into the soil where producers can reuse them. This recycling role is essential for maintaining nutrient availability in agricultural ecosystems.

Question 19Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A legume pasture is included in a crop rotation. What is the main agronomic benefit of this practice?

  1. It increases soil nitrogen through symbiotic fixation Correct answer.
  2. It increases soil salinity
  3. It reduces the need for sunlight
  4. It eliminates all soil-borne pathogens permanently

Explanation

Legumes form symbiotic relationships with nitrogen-fixing bacteria in root nodules, adding nitrogen to the soil that benefits following crops. Rotations also disrupt pest and disease cycles, but nitrogen fixation is the main nutritional benefit.

Question 20Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A farmer measures the water-holding capacity of two soils. Which soil property most directly increases water-holding capacity?

  1. High sand content
  2. Large air spaces between particles
  3. High clay content Correct answer.
  4. Low organic matter

Explanation

Clay particles are small with large surface area and many micropores, which hold water strongly and increase water-holding capacity. Sandy soils with large pores drain rapidly and retain less water.

Question 21Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A graph shows that as nitrogen application increases from 0 to 100 kg/ha, yield rises, but above 150 kg/ha yield levels off. What does the levelling off indicate?

  1. Another factor has become limiting Correct answer.
  2. Nitrogen is now limiting growth
  3. Yield will continue to rise indefinitely
  4. Nitrogen has become toxic at low rates

Explanation

When adding more of one input no longer increases yield, that input is no longer limiting and some other factor, such as water or phosphorus, has become limiting. This is described by the law of the minimum.

Question 22Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student calculates a correlation coefficient of -0.85 between irrigation volume and soil salinity. What is the best interpretation?

  1. Irrigation causes salinity to decrease
  2. There is a weak positive relationship between the two variables
  3. There is no relationship between the two variables
  4. There is a strong negative relationship between the two variables Correct answer.

Explanation

A correlation coefficient of -0.85 indicates a strong negative linear relationship: as irrigation volume increases, soil salinity tends to decrease. Correlation describes association and does not by itself prove that one variable causes the other.

Question 23Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

Which change to an experiment would most improve the precision of the estimated treatment means?

  1. Increasing the number of replicates Correct answer.
  2. Measuring only the largest plants
  3. Removing the control group
  4. Decreasing the number of replicates

Explanation

Increasing replication reduces the standard error of the mean, giving a more precise estimate of each treatment mean. Measuring only selected plants or removing the control would introduce bias rather than improve precision.

Question 24Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A data table reports a mean yield of 4.2 t/ha with a standard error of 0.1 t/ha. What does the standard error indicate?

  1. The range of all individual yields
  2. The probability the result is significant
  3. The precision of the sample mean as an estimate Correct answer.
  4. The largest yield recorded

Explanation

The standard error quantifies how much the sample mean is likely to vary from the true population mean, so a smaller value indicates a more precise estimate. It is not the range of individual values nor a probability of significance.

Question 25Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

An experiment tests whether a new feed additive increases milk production. Which is the most appropriate alternative hypothesis?

  1. The additive is expensive to produce
  2. Milk production varies between individual cows
  3. The additive has no effect on milk production
  4. The additive increases mean milk production Correct answer.

Explanation

The alternative hypothesis states the predicted effect that the experiment is designed to detect, in this case that the additive increases mean milk production. The null hypothesis would state that there is no effect.

Question 26Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student records the mass of 30 pasture samples to the nearest gram using a balance that reads to 0.1 g. Which term best describes how close a single reading is to the true mass?

  1. Precision
  2. Reliability
  3. Accuracy Correct answer.
  4. Validity

Explanation

Accuracy describes how close a measured value is to the true or accepted value, so a reading near the true mass is accurate. Precision instead describes how closely repeated measurements agree with each other, regardless of whether they are near the truth. Reliability refers to consistency of results across repeats or conditions, not closeness to truth.

Question 27Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student wants to compare the mean height of wheat seedlings grown under two light regimes. Which statistical test is most appropriate for comparing the two treatment means?

  1. A line graph with no statistical test
  2. A chi-square test of association
  3. A t-test comparing the two group means Correct answer.
  4. A correlation coefficient between height and light

Explanation

A t-test compares the means of two groups to judge whether any difference is likely to be due to chance, which suits two light regimes with continuous height data. Chi-square tests association between categorical variables, and correlation measures the strength of a relationship between two continuous variables rather than a difference between group means. A graph alone displays data but does not test a hypothesis.

Question 28Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A soil sample is collected from a paddock and its pH is measured three times, giving 6.1, 6.1 and 6.1. A second sample from the same paddock gives 5.4, 5.4 and 5.4. What do these results indicate about the measurements?

  1. They are both precise and accurate
  2. They are accurate but not precise
  3. They are precise but may not be accurate Correct answer.
  4. They are neither precise nor accurate

Explanation

Repeated readings that agree closely with each other are precise, but agreement among repeats does not prove they match the true pH. The two samples differ by 0.7 units, so at least one set is likely biased away from the true value. Precision and accuracy are separate properties, and consistent repeats can still be consistently wrong.

Question 29Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student plots mean pasture yield against stocking rate and obtains a best-fit line with a positive slope. What does the slope of this line represent?

  1. The change in yield per unit increase in stocking rate Correct answer.
  2. The strength of the relationship between the variables
  3. The total yield across all stocking rates
  4. The variation of yield around the mean

Explanation

In a linear model the slope expresses how much the dependent variable changes for each one-unit increase in the independent variable. Here yield is the dependent variable and stocking rate is the independent variable, so the slope is the change in yield per unit of stocking rate. The strength of a relationship is described by the correlation coefficient, not the slope.

Question 30Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student wishes to estimate the average weight of grain in a large silo. Which sampling approach gives the most representative estimate?

  1. Taking one sample from the unloading chute at the end
  2. Taking several small samples from different depths and positions and combining them Correct answer.
  3. Taking a sample from the silo wall nearest the door
  4. Taking one large sample from the top surface only

Explanation

Grain in a silo can vary in moisture and weight with depth and position, so combining several small samples from different locations gives a more representative estimate. Sampling only one location risks bias because that region may not reflect the whole bulk. Representative sampling requires covering the range of variation present.

Question 31Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

In an experiment testing two fertiliser rates on maize, the researcher applies both rates to the same plot on different halves. Which design weakness does this introduce?

  1. The sample size is too large to analyse
  2. The dependent variable cannot be measured
  3. The control treatment is unethical
  4. The treatments are not independently replicated across plots Correct answer.

Explanation

Splitting one plot means the two treatments share the same soil, drainage and edge effects, so their effects cannot be separated and there is no independent replication. Proper design allocates each treatment to separate replicated plots. Sharing a plot confounds treatment effects with position within the plot.

Question 32Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student reports a mean of 32.4 with a 95% confidence interval of 30.1 to 34.7. What does this interval indicate?

  1. The true population mean is likely to lie within this range Correct answer.
  2. The sample mean varies by 95% between replicates
  3. 95% of the individual plants had values between 30.1 and 34.7
  4. The measurements were 95% accurate

Explanation

A confidence interval estimates a range within which the true population mean is likely to fall at a stated level of confidence. It is not a range containing 95% of individual observations, which would be much wider. It also does not measure accuracy or the percentage variation between replicates.

Question 33Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student counts the number of earthworms in quadrats from a paddock under two grazing treatments. Which type of data are these counts?

  1. Nominal categorical data
  2. Discrete quantitative data Correct answer.
  3. Continuous quantitative data
  4. Ordinal categorical data

Explanation

Counts of whole organisms are numerical and can only take whole-number values, so they are discrete quantitative data. Continuous data can take any value within a range, such as mass or length. Categorical data describe categories or ordered groups rather than counts.

Question 34Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A trial compares three irrigation levels and measures plant height. Which graphical display best shows the distribution and central tendency of each treatment group?

  1. A single pie chart of all heights
  2. A scatterplot of height against plant number
  3. A line graph joining individual data points in collection order
  4. Box plots for each treatment Correct answer.

Explanation

Box plots display the median, quartiles and range for each group, allowing distributions and central tendency to be compared across treatments. Pie charts suit proportions of a whole, and scatterplots suit relationships between two continuous variables. Joining points in collection order shows no group comparison.

Question 35Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student finds that plants given a seaweed extract grew taller on average, but the difference was small relative to the variation within each group. What is the most appropriate conclusion?

  1. The extract definitely increases plant height
  2. The extract reduces plant height
  3. The extract has no effect on plant height
  4. The evidence is insufficient to conclude the extract caused a real increase Correct answer.

Explanation

When a difference between means is small compared with within-group variation, it could easily arise from chance, so a firm causal conclusion is not justified. The correct response is to state that the evidence is insufficient rather than to accept or reject the effect outright. A statistical test would be needed to judge significance.

Question 36Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

In a grazing trial, sheep are weighed before and after a feeding period. Which measurement is the dependent variable?

  1. Duration of the feeding period
  2. Type of feed provided
  3. Change in body mass of the sheep Correct answer.
  4. Number of sheep in each pen

Explanation

The dependent variable is the outcome that is measured and expected to respond to the treatment. Here the change in body mass responds to the feed type, so it is the dependent variable. Feed type is the independent variable, and duration and pen numbers are controlled or design factors.

Question 37Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student calculates a mean of 12.4 from five measurements: 10, 12, 13, 14 and 13. What is the range of the data?

  1. 4 Correct answer.
  2. 13
  3. 2
  4. 3

Explanation

The range is the difference between the largest and smallest values in the dataset. Here the largest value is 14 and the smallest is 10, giving a range of 4. The range is a simple measure of spread and ignores all intermediate values.

Question 38Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student wants to compare the proportion of germinated seeds across four seed treatments. Which test is most appropriate?

  1. Paired t-test
  2. Linear regression
  3. Standard error of the mean
  4. Chi-square test of independence Correct answer.

Explanation

Germination counts form categorical frequency data across treatments, so a chi-square test of independence is appropriate for comparing proportions. A paired t-test compares two related continuous measurements, and regression models a relationship between continuous variables. Standard error describes precision of a mean rather than testing proportions.

Question 39Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student measures the length of 50 leaves with a ruler that has worn numbers, causing readings to be consistently 1 cm too high. What effect does this have on the data?

  1. An increase in the sample size
  2. A random error that reduces precision
  3. A systematic error that reduces accuracy Correct answer.
  4. No effect because the error is consistent

Explanation

A consistent offset applied to every reading is a systematic error, which shifts all values in the same direction and reduces accuracy. Random errors scatter readings in both directions and affect precision instead. Consistency does not cancel a systematic bias, and sample size is unrelated to measurement error.

Question 40Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A data set of soil nitrate readings has a mean of 40 mg/kg and a standard deviation of 5 mg/kg. Approximately what proportion of normally distributed values lies within one standard deviation of the mean?

  1. About 50%
  2. About 99%
  3. About 95%
  4. About 68% Correct answer.

Explanation

For a normal distribution, about 68% of values lie within one standard deviation of the mean. About 95% lie within two standard deviations, and about 99.7% within three. These proportions are a standard property of the normal curve and are used to interpret spread.

Question 41Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student records the number of weeds in quadrats placed in a paddock. Which measure of central tendency is most affected by a single very high count?

  1. Range
  2. Median
  3. Mode
  4. Mean Correct answer.

Explanation

The mean uses every value in its calculation, so a single extreme count pulls it strongly toward that value. The median and mode depend on position or frequency and are far less affected by one outlier. The range is a measure of spread rather than central tendency.

Question 42Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student wants to determine whether plant height is related to soil moisture across 20 sampling points. Which analysis is most appropriate?

  1. A chi-square test of the height categories
  2. A t-test comparing two moisture levels
  3. Calculation of the mode of soil moisture
  4. Correlation analysis of the two variables Correct answer.

Explanation

Both plant height and soil moisture are continuous variables measured at the same points, so correlation analysis quantifies the strength and direction of their relationship. A t-test would require dividing moisture into two groups, discarding information. Chi-square and mode calculations do not address a relationship between continuous variables.

Question 43Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

Which statement best describes the purpose of a hypothesis in a scientific experiment?

  1. It guarantees the experiment will produce significant results
  2. It provides a testable prediction about the relationship between variables Correct answer.
  3. It describes the final conclusion after analysis
  4. It replaces the need for a control group

Explanation

A hypothesis is a testable prediction that links the independent and dependent variables and guides the design of the experiment. It does not guarantee any outcome and cannot substitute for proper controls. Conclusions are drawn after analysis, whereas a hypothesis is stated beforehand.

Question 44Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student measures the mass of 10 seedlings twice using the same balance and obtains nearly identical values each time. Which property of the measurement does this demonstrate?

  1. High representativeness
  2. High validity
  3. High precision Correct answer.
  4. High accuracy

Explanation

Precision refers to how closely repeated measurements agree, so nearly identical repeat readings indicate high precision. Accuracy would require knowing the true mass and showing that the readings are close to it. Validity and representativeness concern experimental design rather than repeatability of a single instrument.

Question 45Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A trial reports that a new feed additive increased average daily gain by 0.05 kg/day with a p-value of 0.40. How should this result be interpreted?

  1. The sample size must have been too small to measure growth
  2. The additive clearly improves growth
  3. The difference is likely due to chance and is not statistically significant Correct answer.
  4. The additive has a harmful effect on growth

Explanation

A p-value of 0.40 is well above the conventional 0.05 threshold, meaning a difference this large could easily occur by chance, so the result is not statistically significant. A small numerical difference alone does not establish a treatment effect. The result does not indicate harm, nor does it prove the sample was too small.

Question 46Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student plots the relationship between temperature and the rate of microbial decomposition in soil and finds a curved, non-linear pattern. Which approach best analyses this relationship?

  1. Fitting an appropriate non-linear model or transforming the data Correct answer.
  2. Removing the highest temperature data points
  3. Reporting only the mean decomposition rate
  4. Calculating a single correlation coefficient and stopping there

Explanation

When a relationship is curved, a straight-line correlation or linear model may describe it poorly, so fitting a non-linear model or transforming the data is more appropriate. Reporting only a mean hides the relationship, and deleting high values removes genuine information. A single correlation coefficient may understate a strong but curved relationship.

Question 47Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

In a field experiment, a student uses a buffer strip between treatment plots. What is the main purpose of this strip?

  1. To increase the number of replicates
  2. To eliminate the need for randomisation
  3. To reduce the movement of treatments or pests between plots Correct answer.
  4. To provide extra plants for measurement

Explanation

Buffer strips separate adjacent plots so that applied treatments, water, nutrients or pests do not move between them and contaminate results. They do not add replicates or provide measurement plants. Randomisation is still required to avoid systematic bias in treatment allocation.

Question 48Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student wants to compare the average milk yield of cows fed two different diets, where each cow is measured once. Which design element is most important to ensure a fair comparison?

  1. Feeding both diets to the same cow on the same day
  2. Randomly allocating cows to the two diets Correct answer.
  3. Measuring only the highest-producing cows
  4. Using a single cow for each diet

Explanation

Random allocation distributes individual differences among cows evenly across the two diets, so any difference in yield can be attributed to the diet. Feeding both diets on the same day would confound the treatments, and using single cows gives no replication. Selecting only high producers biases the sample.

Question 49Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

A student records the percentage of organic matter in soil samples from three paddocks and wants to display the data for comparison. Which measure of spread best accompanies the mean for each paddock?

  1. Correlation coefficient
  2. Standard deviation Correct answer.
  3. p-value
  4. Chi-square statistic

Explanation

The standard deviation describes how spread out values are around the mean, so it is the natural companion to a mean for summarising variation within each paddock. The correlation coefficient describes a relationship between two variables, while p-values and chi-square statistics come from hypothesis tests rather than describing spread.

Question 50Unit 3 (internal assessment: Data test IA1, Student experiment IA2)

Which statement best describes why a scientist repeats an entire experiment on a different farm in a different season?

  1. To test whether the findings hold under different conditions Correct answer.
  2. To guarantee the same results will be obtained
  3. To reduce the number of variables measured
  4. To increase the accuracy of a single measurement

Explanation

Repeating a whole experiment under different conditions tests whether the findings are generalisable rather than specific to one site or season. It does not improve the accuracy of an individual measurement or reduce the number of variables. Replication across conditions may well produce different results, so it cannot guarantee identical outcomes.