Free TCE Biology Flashcard Review

It states how a defined independent variable is expected to change a measurable dependent variable under specified conditions. The prediction must be capable of disagreeing with observed evidence.

The independent variable is deliberately varied to test its effect. Controlled variables are held comparable because they could otherwise provide alternative explanations for changes in the response.

Growth could mean height, dry mass or root length, which need not change together. Naming the measurement method and time interval makes the hypothesis and recorded result comparable.

The leaves share one independently treated tree and its conditions. They are useful subsamples, but additional independently allocated trees are needed to estimate variation in the treatment effect between trees.

It reduces systematic assignment of pre-existing differences to treatments. It does not guarantee perfectly identical small groups or replace adequate replication and controlled measurement.

It estimates what happens without the specific tested change while retaining relevant handling and background conditions. This helps separate a treatment effect from time, handling or other shared influences.

A question asks what relationship exists. A hypothesis proposes a testable answer or relationship, often directional, which the investigation can support, challenge or refine.

Mass affects the amount of biological material, while developmental stage affects its activity. Equal masses can therefore have different physiology unless relevant state differences are also controlled or recorded.

It varies two or more factors in combinations, allowing their separate contributions and possible interaction to be examined. For example, shade effects may depend on water availability.

A predeclared measurement-validity rule reduces selective removal of inconvenient results. Document deviations and apply the same rule across groups rather than treating a weak response as evidence of error.

Accuracy concerns closeness to a valid reference or true value. Repeatability concerns agreement among repeated measurements; tightly clustered values can still share a systematic bias.

Averaging can reduce the influence of random fluctuations, but a common calibration offset remains. Independent reference checks are needed to identify and correct systematic measurement problems.

They reveal variation, overlap and unusual observations hidden by one summary. The mean describes central tendency but does not establish that every organism responded identically or that a difference is statistically established.

A percentage-point change subtracts percentages directly. A relative percentage change divides that difference by the starting value: a fall from 60%to 45%is 15 points but a 25%relative decrease.

Groups may differ in confounders, measurement or selection processes, and the temporal direction may be unclear. A causal interpretation needs appropriate design and evidence addressing plausible alternatives.

Participants receive understandable information about the activity and choose without pressure. The approved withdrawal process and protection against disadvantage support their continuing control over participation.

It reduces the chance that measurements can be linked to named individuals by unauthorised readers. Access control and appropriate reporting complement coding; a code alone does not make all reuse acceptable.

Units define what each magnitude means, while the experimental unit shows what is independently replicated. Without them, a plotted spread or rate can be interpreted at the wrong biological level.

Document the change and compare both methods on relevant standards and samples. Apply corrections only over a validated relationship and retain raw data so the combined result can be reviewed.

Report both direction and magnitude, including conditions and variation. Investigate the discrepancy rather than selecting the stronger result or claiming one universal percentage from heterogeneous observations.