17.1 Variation

Phenotypic variation is the difference in observable characteristics between individuals of the same species. The difference may arise from genetic factors, environmental factors, or an interaction between both.

• Genetic differences can be passed to offspring when they are present in the genetic material of gametes, so they can contribute to inherited differences between individuals.
• Environmental conditions can alter how an organism grows or functions without changing the inherited genetic information; those environmental effects are not passed to offspring as the same phenotype.
• A phenotype can reflect both: genotype sets biological possibilities, while the environment affects how those possibilities are expressed.

A useful cautious framework is: phenotype = genotype expressed in an environment. It is not a literal numerical equation and does not mean that every phenotype can be separated into independent genetic and environmental percentages.

Variation is a population-level comparison, not a label that every individual is “different in every way”. This card defines causes of phenotypic variation; it does not classify discontinuous or continuous distributions, explain detailed genetic mechanisms, or test differences between sample means.

Discontinuous variation places individuals into distinct, recognisable categories. Continuous variation gives a measurable range, with intermediate values between the extremes.

• Discontinuous: the phenotype is qualitative and category-based; a value does not fall between the categories. Example: ABO blood groups.
• Continuous: the phenotype is quantitative and measured on a scale; individuals can take values across a range. Examples: human height or mass.
• In a table or graph, separated categories support a discontinuous interpretation, whereas a spread of values across a range supports a continuous interpretation.

The pattern describes what is observed, not automatically why it occurs. Discontinuous traits are often associated with large effects at one gene locus, while continuous traits commonly reflect many small genetic effects together with environmental influence; the genetic basis must be checked rather than inferred from the appearance alone.

Continuous does not mean “not genetic”, and discontinuous does not by itself prove a single-gene cause. This card classifies the observed variation; it does not develop the detailed genetic architecture or apply a t-test to sample means.

Genetic variation is variation in the DNA base sequence or allele combinations between individuals of a species. It can create different inherited possibilities for phenotype; environmental conditions then affect how those possibilities are expressed.

• Mutation changes a DNA base sequence and can create a new allele. For the change to be inherited, it must occur in genetic material that contributes to gametes.
• Crossing over, independent assortment and random fertilisation create new combinations of existing alleles, rather than automatically creating new alleles.
• A gene is a DNA sequence at a locus; different alleles are alternative versions of that sequence. Their effects can be large at one locus or small and additive across several genes.

Discontinuous phenotypes are commonly associated with a large effect at one gene locus, whereas continuous phenotypes commonly reflect many small genetic effects together with environmental influence. This is a tendency within the syllabus boundary, not a cause that can be diagnosed from the phenotype alone.

Recombination reshuffles existing alleles; mutation can introduce a new allele. Environmental effects can change phenotype without changing the inherited DNA and are not themselves inherited as the same phenotype. Detailed t-test calculations and natural-selection frequency changes are outside this card.

A t-test compares the means of two samples to judge whether their difference is larger than would be expected from variation within the samples. It tests the difference between means, not whether the data are biologically important by themselves.

• Use the test for two sets of continuous data that are approximately normally distributed, with approximately equal standard deviations; calculate a standard deviation for each sample.
• State the null hypothesis: there is no statistically significant difference between the two population means, and any observed difference is due to chance.
• Design the comparison fairly: keep relevant conditions the same, use comparable samples and sample sizes where appropriate, and identify the measured variable before analysing the means.

• Calculate or obtain each sample mean and standard deviation, then use the supplied t-test formula.
• Calculate degrees of freedom: v = (n₁ − 1) + (n₂ − 1).
• Compare the calculated t with the critical value for v at the chosen significance level. If t is greater than the critical value, reject the null hypothesis; otherwise, do not reject it.
• State the conclusion about whether the difference between the two means is statistically significant and relate it to the experimental comparison.

Rejecting the null hypothesis supports a statistically significant difference; it does not prove causation or establish biological importance. Failing to reject it does not prove that the means are identical. Do not say that “the data” are significant: the difference between the means is significant or not.