Pattern Seeking

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Learning Intentions

By the end of this topic, you should be able to:

Define what a fair test is and explain why it is important in science.
Identify independent, dependent, and control variables in an experiment.
Design a fair test by changing only one variable and keeping all others the same.
Explain how to make an experiment valid and reliable.
Evaluate the advantages and limitations of fair testing in different types of investigations.
Apply the concept of fair testing to real-world examples, such as comparing products or testing scientific ideas.

Glossary

Key terms and definitions.

causation: When one event directly causes another to happen (cause and effect).

correlation: A connection or relationship between two factors, though one does not necessarily cause the other.

data: Information or measurements collected during an investigation.

fair test: An investigation where only one variable is changed and all others are kept the same.

pattern seeking: A method of investigation used to look for patterns or relationships between variables that cannot easily be controlled.

population: The entire group of individuals or items being studied.

random sampling: Selecting a sample so that every individual or location has an equal chance of being chosen.

sample: A smaller group taken from a population to represent it in an investigation.

sample size: The number of individuals, measurements, or data points collected in a study.

system science: A field of science that studies complex natural systems, such as weather, ecosystems, or the solar system.

variable: A factor that can change or vary in an investigation.

What is pattern seeking?

Turning ideas and thoughts into scientific knowledge involves a process called investigating. Pattern seeking is the investigation approach used to find relationships and links between two or more factors (variables) - especially when these variables are difficult or impossible to control. This makes it a useful approach in system sciences such as ecology, geology, meteorology, and astronomy, where it is impossible or unethical to control natural conditions.

Unlike fair tests, where one variables is deliberately changed and the others are kept the same, pattern seeking focuses on observing and recording natural events to look for patterns or trends in the data.

Pattern seeking helps scientists identify correlations (connections) that might exist in nature — for example:

The relationship between rainfall and the growth of native plants
The types of rock found at different parts of a hillside
The phases of the Moon and its position in the sky

These patterns can help scientists form hypotheses (ideas) that can later be tested through other types of investigations.

How do pattern seeking investigations work?

In a pattern seeking investigation, scientists begin by observing something in the real world and identifying variables that might be linked to those observations. They then collect data systematically, often through sampling, and analyse it to look for patterns or trends.

Once a pattern has been identified, it can lead to new questions or investigations to explain why the pattern occurs. Once the pattern is described, it could also be modelled to allow further investigations to be carried out. This makes pattern seeking an important first step in many areas of scientific research.

Unlike fair tests, pattern seeking usually does not manipulate variables. Instead, it focuses on what naturally occurs and how different factors might be related.

Example: Ecology

Scientists might study mangrove pneumatophores (breathing roots) to see whether they are denser in areas with different drainage levels. They do not change the environment — they just record where and how the plants grow, then look for a pattern.

Example: Astronomy

By observing the phases of the Moon over time, students can find a repeating pattern and use it to develop a model that explains why the phases occur.

Why is pattern seeking important?

Pattern seeking investigations are particularly common when studying living things or natural environments because it is not always possible to control all the variables. For example, when investigating humans, you can choose participants of the same age or sex, but it is much harder to ensure they all have the same fitness, sleep, or diet before the investigation.

Similarly, when studying animal behaviour at the beach, it is impossible to control environmental factors such as weather, tide, and time of day, all of which may influence the results. In these situations involving complex systems (like ecosystems and the weather), scientists record what they observe and later look for relationships in the data.

Pattern seeking investigations are especially useful in system sciences, such as:

Ecology - the study of relationships between organisms and their environments
Geology - the study of rock patterns or plate tectonics
Meteorology - the study of weather patterns
Astronomy - the study of objects and patterns in space

Correlation vs. Causation

Pattern seeking is about exploring possible relationships between two variables — in other words, investigating whether one factor appears to be linked to another. For example:

Does age affect height?
Does leg length affect how high someone can jump?
Are snails more commonly found in dark places than in bright ones?

When you notice a pattern between two factors, this is called a correlation. However, correlation does not always mean causation — just because two things happen together doesn’t mean one causes the other.

Example: Ice Cream Sales and Jellyfish Stings

A well-known example of this idea is the relationship between ice-cream sales and jellyfish stings. Data often show that when ice-cream sales increase, so do the number of jellyfish stings. However, ice-cream does not cause jellyfish stings, nor do jellyfish stings cause more ice-cream sales! Instead, the two are linked by a third factor — warm weather — which increases both swimming and ice-cream buying.

This is an example of a correlation, not causation. Causation means that one event directly causes another to happen, whereas correlation simply means the two factors change together. Scientists must always be careful to distinguish between the two.

Populations, Sampling and Sample Size

In statistics, a population is the entire collection of objects or individuals that we are interested in, in an investigation. 'Populations' can apply at different levels. A research study on bees might be interested in all the beehives in a particular area. This collection of beehives is the 'population', and each beehive is an 'individual'. Another study might just look at the worker bees within a single hive. Here, the population is the collection of all the worker bees in that hive, and each worker bee is an individual.

Because pattern seeking investigations often study large populations or natural environments, it is not possible to collect data from every individual. Instead, scientists use sampling, where a smaller subset of the population is studied. The number of individuals or measurements collected is called the sample size.

Random sampling

When sampling from a population, it is important that the sample is representative of the population. Samples should be chosen randomly, meaning every object or individual has an equal chance of being selected. Random sampling helps to avoid bias and ensures that the data more accurately reflect reality.

Large sample size

Another important principle in sampling is the effect of sample size. The larger the sample size, the more likely it is that the sample will be representative of the population. With samples of just a few objects or individuals, there will be a lot of random variation. A sample of a large number of objects or individuals is more likely to have a composition that is similar to that of the whole population. Choosing a small sample means that collecting data is easier, but with larger samples, there is more confidence that the data are representative. In practice, sample size is determined by the balance between these two factors.

Example: Sampling at School

For example, if you wanted to find out whether age affects height at your school. Here is the total population at Smallville High School:

You probably won't have enough time to collect the measurements of every student in the school. Instead, a sample (sub-set of the whole) can be taken. You could select a random sample by drawing names randomly from a box rather than choosing your friends, because that might give misleading results.

The sample size should be as large as possible, for the time you have available to collect data. A larger sample size is a more reliable representation of the whole population. In this example, when 5 individuals were sampled, by chance taller people were selected (red). This did not reliably reflect the whole school. Whereas when 40 individuals were selected, more of a mix of tall and short people were selected, better reflecting the school population.

Example: Sampling in Nature

In environmental studies, scientists can divide an area into a grid and use random coordinates to decide where to collect data. An area can be divided up into a grid, with each section given a number and letter. A number and letter are then randomly selected and that square is sampled. This sampling is repeated as many time as possible, within the timeframe you have.

What are the advantages and limitations of pattern seeking investigations?

There are both advantages and limitations to using the pattern seeking method of investigation.

Advantages

One advantage is that it is well suited to studying natural systems where variables cannot be controlled. It allows scientists to identify possible relationships and can lead to new questions and discoveries. Pattern seeking investigations also help scientists develop models to explain natural phenomena, such as the phases of the Moon or the distribution of plants in different habitats.

Disadvantages

However, pattern seeking investigations can also be challenging. Because it is difficult to control all variables, it can be unclear which factors are influencing the results. Collecting large sample sizes can take considerable time, and data from natural environments can become complex and hard to interpret. Some results rely on human judgement, such as identifying species or counting individuals, which can introduce errors. Finally, pattern seeking only shows correlation, not causation, so further investigation is often needed to find out why a pattern occurs.

Despite these challenges, pattern seeking is an essential part of scientific inquiry. It helps scientists understand how different aspects of the natural world are related and how they influence each other. It can also lead to other types of investigations, such as fair tests, to explore the reasons behind the patterns that have been observed.