The Scientific Method Lab 5

The Scientific Method and Experimental Design

Introduction

The scientific method is a systematic approach used by biologists and other scientists to investigate natural phenomena, answer questions, and solve problems. This process involves making observations, forming hypotheses, testing those hypotheses, analyzing data, and communicating results. Understanding the scientific method is fundamental for all students of biology.

Study Questions Overview

• How science differs from other fields of inquiry

• Acquisition and communication of scientific knowledge

• Presenting data in tables and graphs

• Summarizing scientific studies

• Distinguishing between hypotheses and theories

• Types of scientific hypotheses

• Experimental variables: independent, dependent, control

• Examples of variables in experiments

• Role of correlation in evidence

• Importance of repeated experiments

• Application of the scientific method to case studies

Discovery Science: Making Observations & Finding Patterns

Discovery science involves careful observation and data collection to identify patterns in nature. This approach is foundational in biology, as it allows scientists to generate questions and hypotheses based on empirical evidence.

• Objective, repeatable, and consistent observations are essential for scientific validity.

• Data are often organized in tables and graphs to reveal patterns and relationships.

Good Tables Should Include:

• Informative overall title and titles for columns/rows

• Units of measurement

• Summary of numerical values

Good Graphs Should Include:

• Clear sense of pattern

• Informative titles and axis labels (with units)

• Appropriate graph type for the data

• Distinction between independent (X-axis) and dependent (Y-axis) variables

Example:

A graph showing the effect of fertilizer on the growth of corn plants, with fertilizer dose on the X-axis and plant height on the Y-axis, helps visualize the relationship between these variables.

Making Hypotheses

After making observations, scientists propose hypotheses—testable explanations for observed phenomena. Hypotheses must be specific, testable, and falsifiable.

• Hypothesis: A tentative explanation that can be tested by further investigation.

• Must be testable and falsifiable.

• Can be supported but never proven with absolute certainty.

Examples of Hypotheses:

• Trees lose their leaves because this helps prevent branches from breaking in the winter.

• Birds migrate because their food supply changes seasonally.

• Whales migrate because they like to swim.

Testing Hypotheses: Experimental Design

Testing hypotheses involves designing experiments that compare two or more groups differing only in the variable being tested. This allows scientists to determine cause-and-effect relationships.

Types of Variables:

• Independent Variable: The factor that is changed or manipulated by the experimenter.

• Dependent Variable: The response or outcome measured in the experiment.

• Controlled Variables: Factors kept constant to ensure that observed effects are due to the independent variable.

Example Table: Types of Experimental Variables

Variable Type	Definition	Example
Independent	Manipulated by experimenter	Amount of fertilizer applied
Dependent	Measured response	Plant height
Controlled	Kept constant	Soil type, water amount

Experimental Groups:

• Reference/Control Group: Not exposed to the independent variable.

• Positive Control: Expected to show a known response.

• Negative Control: Expected to show no response.

Example: Camouflage and Predation in Mice

Researchers tested whether camouflage protects mice from predators by placing light and dark models in different habitats and recording predation rates.

	Beach Habitat	Inland Field Habitat
Dark Model	56%	24%
Light Model	24%	56%

Proportion of attacks on camouflaged and non-camouflaged models in different habitats.

Correlational Evidence

When controlled experiments are not possible, scientists may rely on correlations to infer relationships between variables. However, correlation does not imply causation.

Examples:

• Global Temperature and CO2: Correlation between rising atmospheric CO2 and global temperature supports the hypothesis that CO2 contributes to warming.

• Smoking and Cancer: Correlation between smoking rates and cancer incidence supports the hypothesis that smoking causes cancer.

Communicating Scientific Knowledge

Scientists communicate their findings through peer-reviewed publications, presentations, and other media. Scientific papers typically include an abstract, introduction, methods, results, and discussion.

• Primary sources: Original research articles published in scientific journals.

• Secondary sources: Reviews, textbooks, and summaries of primary research.

Making and Applying Generalizations

Generalizations allow scientists to infer broader patterns from specific observations. Once a generalization is established, it can be used to make predictions about new situations.

• Generalizations must be based on repeated, consistent evidence.

• They are powerful tools for scientific reasoning and application.

Key Terms and Definitions

• Hypothesis: A testable explanation for an observation.

• Theory: A well-supported, broad explanation for a wide range of phenomena.

• Variable: Any factor that can change in an experiment.

• Control Group: The group in an experiment that does not receive the experimental treatment.

• Correlation: A relationship between two variables.

Formulas and Equations

• Mean (Average): $\text{Mean} = \frac{\sum x_i}{n}$

• Proportion: $\text{Proportion} = \frac{\text{Number of events}}{\text{Total number}}$

Summary

• The scientific method is a logical, evidence-based approach to understanding the natural world.

• Experiments must be carefully designed with appropriate controls and variables.

• Data should be clearly presented in tables and graphs.

• Scientific knowledge is communicated through peer-reviewed publications and is subject to revision as new evidence emerges.