A Level only mathematical and statistical requirements

Probability helps predict genetic ratios and quantify how chance affects a biological sample.

Separate expected probability from the result observed in one sample, and increase sample size when chance variation is large.

A 1:1 genetic ratio does not require every small family to contain equal numbers of each phenotype.

Chance variation is not evidence that the genetic model is wrong by itself.

Hardy–Weinberg estimates allele/genotype frequencies, Lincoln estimates a mobile population and Simpson’s index summarises diversity.

Define each symbol from the data, substitute consistently and interpret the result in the biological context.

A recapture estimate becomes unstable when the number marked in the second sample is very small.

A formula output is only as reliable as the assumptions behind sampling and the data collection.

Mean, median, mode, range, standard deviation, standard error and confidence intervals describe different aspects of a dataset.

Use the statistic that matches the distribution and explain what error bars represent before comparing them.

A skewed dataset may be better summarised by a median, while standard error describes uncertainty in a mean rather than spread of individual values.

Error bars are not interchangeable; SD, SE and confidence intervals answer different questions.

Chi-squared compares observed and expected categories; a t-test compares means under suitable assumptions.

Calculate degrees of freedom, compare the probability with the significance level and state the biological decision.

Rejecting the null for a chi-squared test means the observed categories differ from expectation; it does not identify the mechanism.

A significant result does not show that the effect is large or biologically useful.

Pearson and Spearman coefficients describe relationships from −1 to +1, but neither alone establishes causation.

Choose the test that matches data type and pattern, inspect confounding variables and state conditions for validity.

Plant abundance may correlate with soil moisture because both respond to altitude rather than because moisture alone causes the pattern.

A strong correlation can be spurious, and a weak correlation can hide a non-linear relationship.