A Level formula reference and statistics notes

Use p+q=1 and p²+2pq+q²=1 to connect allele and genotype frequencies.

Identify whether the supplied value is an allele frequency, homozygous genotype frequency or heterozygote frequency before rearranging.

If q² is known, take the square root to find q, then calculate p and 2pq.

The equations are a model with assumptions; they are not a universal description of every population.

Use N=(n₁×n₂)/m₂ for mark-release-recapture and the stated Simpson formula for diversity.

Define each sample count and check that the formula version matches the convention in the question.

If few marked animals are recaptured, the estimate can become very large and uncertain.

Never interpret Simpson’s direction without checking whether the formula is D, 1−D or a reciprocal form.

Apply the provided formulae to calculate a statistic, then interpret the size or interval rather than memorising symbols in isolation.

Keep observed and expected values paired and preserve units for biological quantities.

A confidence interval around a mean expresses uncertainty in that estimate, not the full spread of individual observations.

A correct calculation can still be misinterpreted if degrees of freedom or the null hypothesis is wrong.

Use the supplied formula for the selected test and calculate degrees of freedom where required.

The final step is a decision about significance and biological meaning, not just a number.

A Pearson coefficient near +1 indicates a strong positive linear association under valid conditions; it does not prove one variable causes the other.

Do not choose a test because its formula is familiar; choose it from the data type and question.

Statistical validity depends on data type, distribution, independence, sample size and whether the question asks for a difference or relationship.

State why a method fits before calculating, then link the outcome to the biological claim.

Chi-squared suits category counts, a t-test suits two means, and Spearman suits ranked or non-normal monotonic data.

A method can be mathematically executable but biologically invalid if its assumptions are ignored.