Biology (9700) | Variation

A-Level Biology (9700) Study Notes: Variation

Hello future Biologists! Welcome to the chapter on Variation. This is a crucial topic because variation is the fundamental engine that drives evolution and natural selection. By the end of this chapter, you will understand why no two organisms (even siblings!) are exactly alike and how we categorize and measure these differences. Don't worry if statistics are involved later—we'll break down the math into simple, manageable steps!

17.1 Understanding Phenotypic Variation

The term phenotype simply means the observable characteristics of an organism (what it looks like, how it behaves, or how its body works). Variation refers to the differences in phenotypes among individuals in a population.

1.1 The Two Main Causes of Variation

Phenotypic variation in a population is caused by three possibilities:

1. Genetic Factors
These are variations inherited from parents, determined by the different alleles an individual possesses. They are permanent and cannot be changed by the environment.

• Examples: Human blood group (A, B, O), eye colour, inherited diseases.

2. Environmental Factors
These are variations caused by external conditions. The environment interacts with the organism's genetic potential to determine the final phenotype.

• Examples: Scars (accidents), language spoken, amount of sunlight affecting plant height (even if genetically tall).

3. Combined Genetic and Environmental Factors (G x E)
Most characteristics are influenced by both. The genes set the potential range, but the environment determines where in that range the phenotype falls.

• Analogy: Think of a rubber band. Your genes determine how stretchy the rubber band is (the maximum potential). The environment (like diet and exercise) determines how much you actually stretch it.
• Examples: Height (genes provide potential, nutrition affects growth), weight, intelligence.

Quick Takeaway: Variation is essential for natural selection to act upon. Without differences, there is nothing to select for or against!

17.1.2 & 17.1.3 Classifying Types of Variation

We classify variation into two main types based on how the characteristic values appear within a population.

2.1 Discontinuous Variation

Discontinuous variation results in clear-cut, separate categories. There are no intermediate values.

• Key Features:
  • Qualitative: You can count the number of categories.
  • Distinct Groups: Individuals fall into one category or another (e.g., you are either blood type A or blood type B).
  • Unaffected by Environment: Largely controlled solely by genetic factors.
  • Graph Shape: Usually shown as a bar chart.
• Genetic Basis: Determined by the alleles of a single gene (or a very few genes).
• Examples: Human blood groups (ABO system), ability to roll your tongue, flower colour in certain pea plants.

2.2 Continuous Variation

Continuous variation results in a range of values between two extremes. The measurements are gradually graded.

• Key Features:
  • Quantitative: You can measure the values along a scale.
  • Intermediate Values Exist: There is a smooth transition between the smallest and largest (e.g., someone can be 170.1 cm or 170.2 cm tall).
  • Strong Environmental Influence: Often significantly affected by environmental factors (G x E).
  • Graph Shape: When plotted, it typically results in a normal distribution curve (bell-shaped curve or histogram).
• Genetic Basis: Determined by the interaction of many genes, known as polygenic inheritance. Each gene has a small additive effect on the phenotype.
• Examples: Height, mass/weight, leaf length, skin colour, running speed.

Memory Aid: Continuous = Curve (Bell-shaped) and Countless Genes (Polygenic).

17.1.4 Statistical Analysis of Variation: The t-test

In Biology, when we collect data on continuous variation (like measuring the height of two plant populations), we often want to know if the difference between their average values (means) is meaningful, or just due to chance.

This is where the t-test comes in. The t-test is used to compare the means of two different samples to see if there is a statistically significant difference between them.

The Purpose and Application of the t-test

Imagine you tested two different types of fertilisers (A and B) and measured the final heights of the plants. Fertilizer A plants were, on average, 2 cm taller than Fertilizer B plants. Is this small difference actually important, or did it just happen because of random chance in the sample?

The t-test answers this by testing a statement called the Null Hypothesis (H₀).

H₀ (Null Hypothesis): There is no significant difference between the means of the two samples (i.e., any observed difference is purely due to chance).

H₁ (Alternative Hypothesis): There is a significant difference between the means of the two samples.

Step-by-Step Procedure (Conceptually)

Don't worry about memorising the complex formula—it will be provided in the exam. Focus on these steps:

Step 1: Calculate the t-value (using the provided formula)
This calculation takes into account the means of the two samples, the standard deviation (spread) of the data, and the sample size. The resulting value is the calculated t-value ($t_{\text{calculated}}$).

Step 2: Determine the Degrees of Freedom ($v$)
This is based on the total number of individuals in both samples minus 2.
\[ v = n_1 + n_2 - 2 \]

Step 3: Find the Critical Value
You use the calculated degrees of freedom ($v$) and the chosen level of probability (usually 5% or $p=0.05$) to find the critical value ($t_{\text{critical}}$) from a statistical table (which is also usually provided).

Step 4: Draw a Conclusion
Compare your calculated $t$-value to the critical $t$-value:

• If \( |t_{\text{calculated}}| \) > \( t_{\text{critical}} \): The difference is significant. You must reject the Null Hypothesis (H₀). This means the difference observed is likely due to the factor you were testing (e.g., the fertilizer really did make a difference).
• If \( |t_{\text{calculated}}| \) \(\le\) \( t_{\text{critical}} \): The difference is not significant. You accept the Null Hypothesis (H₀). The difference is likely due to chance.

Note: The absolute value is used because the difference could be positive or negative depending on which mean you subtract first.

Did you know? The 5% probability level ($p=0.05$) means that if you reject the null hypothesis, there is still a 5% (1 in 20) chance that you were wrong, and the difference actually occurred by chance. Biologists accept this level of uncertainty as standard for significance.

Key Takeaway: The t-test is our tool for proving that an observed difference in continuous data between two groups is real (statistically significant) and not just a random fluke.