Planning an Pattern-Seeking Investigation

What is a Pattern-Seeking Investigation?

First of all, a scientific investigation includes the process of observing, hypothesising and experimenting.

This is important because all of our knowledge about the natural world has been gathered using this process.

Previous explanations/theories can be modified when new evidence is presented.

Pattern-seeking investigations involve observing and recording natural events or carrying out experiments where the variables can’t easily be controlled.

In pattern seeking, it is still important to note and record variables or factors. The investigator needs to try to identify patterns that result from these variables.

Pattern seeking can help us create models to explain observations, like explaining the successive communities of plant species over time (ecological succession).


The hypothesis is an educated guess, as to the likely outcome of the investigation.

Hypotheses must always be a statement, never a question.

If the hypothesis is to be of any use, it needs to lead somewhere, either to a positive or negative conclusion. You must be bold, and make a prediction which you can then prove right or wrong with an investigation.

You can learn a lot from a negative hypothesis - you have eliminated something so you to make another hypothesis to test, and so on until a picture of the truth starts to emerge.

For Checkpoint 1, carry out a pattern-seeking investigation about the following research question:

What is the difference in plant abundance and diversity in a more recently disturbed and less recently disturbed site at Massey High School?

  • Which site do you think will have greater plant abundance?

  • Which site do you think will have greater plant diversity?

  • Why do you think this?

Variables & Method


The investigation must be set up with certain criteria:

  1. It must be controlled by having controlled variables. These are the variables that must be held constant, so they do not affect the experimental results.

  2. You must only have independent variable. This is the one variable that is changed by the investigator. It must have an appropriate range to ensure valid results.

  3. You may have more than one dependent variable in pattern-seeking investigations. This is the variable measured by the investigator.

  4. Your sample size must be as large as possible for the experiment to be statistically valid.


The main point of the method is to provide enough detail for someone else to be able to copy exactly what you did in every step and in every detail.

You should include the site locations of each sample, how many samples were taken, details of specialised equipment used, how data was recorded, how plant species were identified etc.

A method is much easier to follow if you write out the method in a step-by-step manner.

The FINAL method should be written in past tense.

While conducting your investigation, think about how you will make sure your data is:

  1. Valid - Are you measuring what you are supposed to be measuring?

  2. Reliable - Are your results consistent?

Data Collection & Results

You will be collecting and presenting two types of data:

  • Quantitative - what can be measured or counted. For these internals, you must neatly present your data as a table. It is optional to graph your results.

  • Qualitative - what can be described. This can be done in words, drawings, or in photographs of something observed.

Raw data is the data you collect during the investigation, and your teacher will provide you with this template for your data collection.

Data collection sheet_SUCCESSION.docx

As per the template, you will be collecting the following data:

  • Biotic data

    • Diversity of plant species (by identifying all of the different plant species within a given range; qualitative).

    • Quantity of each plant species within a given range (either by count or % coverage; quantitative).

    • Descriptions of plant adaptations observed (qualitative).

  • Abiotic data

    • Temperature - using a thermometer (quantitative)

    • Light intensity - using a light meter (quantitative)

    • Humidity - using a hygrometer (quantitative)

    • Weather conditions (qualitative)

Massey High_20220426_151213.pdf

Your raw data should then be processed in such a manner that it enables the reader to determine the presence or absence of a trend. This is usually accomplished by:

  • Calculating the averages (highly recommended for this internal)

  • Plotting graphs (optional for this internal)


The conclusion summarises the findings presented in the results section. It is a written description of the processed data and should refer to the hypothesis of the investigation.

  • What does your data show?

"Our results show..."

  • The conclusion should outline whether the data supports or rejects the hypothesis.

"This supports our hypothesis ______ because..."

The conclusion should be to the point - and only what can be concluded from the investigation. Therefore you must refer to your data. You must write a conclusion for plant abundance and plant diversity.


A method/investigation is valid if it measures what it is supposed to measure. Validity is dependent on:

Sampling appropriately

Remember that the research question is: What is the difference in plant abundance and diversity in a more recently disturbed and less recently disturbed site at Massey High School?

To be totally sure of the plant abundance and plant diversity, you would need to survey every single organism in the population. But as you might imagine, this will be very difficult because:

  • The populations are very large - there are many many many individual plants at Massey High School.

  • The populations are spread over a large area

  • The individual plants can be well hidden.

To overcome the problems involved with total surveys of populations, biologists have developed an alternative approach called 'sampling'.

Sampling randomly

Minimising bias

Measuring and recording data accurately and precisely.

Discussion of Biological Ideas

The discussion is where you interpret your results using relevant biological concepts and knowledge (i.e. ecological succession). You should:

  • Analyse and describes the trends, patterns and results - you must refer to processed data.

  • Use biological concepts (i.e. ecological succession) when describing trends.