Hypothesis and experimentation

The first statement clearly defines the changed factor and measured outcome. In an operational hypothesis identify quantity duration and measurement method.

Particle size can also affect dissolving time. Keep sugar particle size constant to isolate the effect of temperature.

The group without fertiliser provides a normal baseline. A control group supports fair comparison of the treatment effect.

Actual height gain is obtained from the difference between initial and final values. Recording a baseline is necessary for fair comparison.

The term healthier lacks a clear measurement method. Replace it with an outcome such as height leaf number or mass.

Selecting only favourable results does not represent the whole group. Record outcomes for all seeds using the same rule.

Changing one main factor at a time keeps the cause of the result clear. This is the basis of a fair experiment.

Inconsistent results may arise from the procedure or uncontrolled factors. Check the method and instruments before repeating the test.

The direct answer is B: an average can be misleading when one extreme unusual value exists and its cause has not been checked. An average is found by adding all values and dividing by the number of values, but an extreme value can pull the result upward or downward. For example, measurements of 10, 11, 10 and 49 have an average of 20, although most observations are close to 10 or 11. Therefore the average alone may not represent the usual result. Option B is correct because the unusual value needs investigation before the average is interpreted; it may be a mistake or a genuine variation. Option A is wrong because similar measurements usually give a representative average. Option C is wrong because using the same unit improves, rather than harms, comparison. Option D is wrong because repetition generally provides more evidence, although it still must be done carefully. Memory cue: inspect extreme values before trusting an average.

A consistent trend across repeated trials provides strong evidence. A single result is not sufficient.

One sample may not capture natural variation. More samples make general conclusions more reliable.

Pot size may independently affect plant growth. This makes the effect of water amount unclear.

The second statement clearly defines the amount of change duration and measurable outcome. It supports a direct prediction.

Without clear steps another person cannot perform the same investigation. Record quantities timing instruments and sequence.

Differences in plant type may affect growth. Both groups should contain the same type of plant.

Consistent opposing evidence from several controlled trials makes a hypothesis unsupported. One trial alone is not sufficient.

The direct answer is A: calibration checks an instrument reading against a known value. Calibration means comparing a measuring instrument with a trusted reference, such as checking a thermometer against a known temperature or a balance with a standard mass. Step by step, use the reference, read the instrument, compare the two values, and adjust, label or replace the instrument if necessary. Option A is correct because calibration tests measurement accuracy and helps reveal systematic error. Option B is wrong because calibration cannot make a hypothesis true; evidence must decide whether a hypothesis is supported. Option C is wrong because calibration does not determine or reduce the number of samples. Option D is wrong because calibration does not remove the control group; the control group serves a separate experimental purpose. Calibration improves confidence in measurements but does not guarantee that every experiment is perfect. Memory cue: calibrate means compare with a known standard.

A thermometer may need time to reach the liquid temperature. Record the value after the reading stabilises.

Morning and evening conditions may differ. Observe both groups at the same time for a fair comparison.

A limited experiment does not justify universal claims such as always and every type. Keep the conclusion within the scope of the investigation.

Random selection reduces preference based sampling bias. It helps the sample represent the whole group better.

Without original values variation and unusual results cannot be examined. Keep all trial values along with the average.

A flawed method may repeatedly give similar results but still fail to measure the intended effect correctly. Reliability and validity are different qualities.

The initial water volume should remain the same so the effect of width can be compared fairly. Keep temperature constant as well.

The first statement specifies controlled conditions and a measurable outcome. Avoid overly universal claims.