SCIENTIFIC INQUIRY SKILLS
Science Inquiry SkillsHuman Biology ATAR - Unit One
Mr. Clarke
Thinking Like a Scientist
Characteristics of people who think like scientists:
Do not accept everything without question.
Understand the significance of evidence.
Appreciate the importance of knowledge.
Maintain a sense of wonder and curiosity toward the natural world.
Are not afraid to think critically.
Fundamental Skills in Scientific Inquiry Observation
Definition: Taking in information from the environment using the five senses: touch, taste, hearing, sight, and smell.
Key Note: If information cannot be perceived through these senses, it does not qualify as an observation.
Classification
Definition: The process of grouping items based on shared characteristics.
Example: Arteries, veins, capillaries, platelets, the aorta, and the pulmonary vein are all classified under the circulatory system.
Hypothesis
Definition: A concise statement addressing a problem or question.
Characteristics:
Not posed as a question.
Should be short and direct.
Must include both the Independent Variable (IV) and Dependent Variable (DV).
Can be structured as an “If … then” prediction.
Example: A hypothesis might be written as "If the concentration of a solution increases, then the rate of diffusion will increase."
Inference
Definition: The logical interpretation of data, explaining the results within the context of the study.
Key Difference: Inference involves explaining "why" certain data patterns are observed, while a conclusion concerns what the data indicates directly.
Variables in Experiments
General Definitions
Variables: Factors that can change or be controlled in an experiment.
Independent Variable (IV): The variable that researchers manipulate or change during the experiment.
Dependent Variable (DV): The variable that is measured and is expected to change as a result of alterations to the independent variable.
Control Group
Definition: A baseline group against which the experimental group is compared.
Important Notes:
A good scientific experiment requires at least one control group and one experimental group.
The control group does not receive the treatment, allowing scientists to determine if the independent variable has an effect.
Placebo
Definition: An inactive or dummy treatment designed to resemble the actual medication.
Characteristics:
Administered in the same manner as the real drug.
Can include substances that are not drugs at all.
Used primarily in control groups, where participants do not know if they are receiving the actual treatment or the placebo.
Controlled Variables
Definition: Variables that remain constant throughout an experiment to ensure that the results are not influenced by external factors.
Purpose: Controlling variables is essential to minimize the potential for confounding variables that might skew data.
Scientific Method in Practice
Example Study (2008)
Study Focus: Investigating the safety and effectiveness of a contraception injection on sperm count in 320 healthy men.
Participant Criteria:
Age: 18 – 45 years old.
Relationship: Must be in a monogamous relationship of at least 1 year.
Pre-study testing required normal sperm count (>15 million/mL).
Procedure: Injection of 1200 mg of synthetic hormones given intramuscularly every 8 weeks.
Study Questions:
Outline the:
a. Independent Variable
b. Dependent Variable
c. Controlled Variables
Formulate a suitable hypothesis.
Key Concepts in Data Analysis
Objectivity
Definition: Maintaining impartiality in scientific observations and results; avoiding personal bias.
Validity, Accuracy & Reliability
Validity: The extent to which the experiment measures what it is intended to measure.
Note: An experiment cannot be considered valid if it is not reliable.
Reliability: The consistency of results across multiple trials of an experiment.
Accuracy: The degree to which controlled variables are effectively managed and the appropriateness of conducted methods.
Replication of Results
Purpose: Repeating experimentation to establish the validity and reliability of the findings and to eliminate errors during the study.
Interpreting Data
Key Statistics:
Median: The middle number of a data set arranged in ascending order, useful for reducing the impact of outliers.
Importance: Enhances accuracy of data representation.
Analyzing Results
Key metrics for evaluation:
Average
Median
Range
Ratio
Rate
Percentage
Percentage change
Frequency
Probability
Presentation of Data
Essential Components:
Tables: Should always include a title.
Graphical Representation: Understand whether to use line or bar graphs based on data type.
Models: Use flow charts when necessary for elucidation.
Bar Graph vs. Line Graph
Bar Graph:
Used for discrete data (non-continuous values).
Examples include categories such as colors or types of animals.
HINT: Independent variables (IV) are usually categorical words.
Line Graph:
Used for continuous data (values can take any number).
Examples include measurements like height and temperature.
HINT: Independent variables (IV) are typically numerical.
Data Examples
Blood Flow (%) During Exercise:
Skeletal Muscle: 70 (exercise) vs. 15 (rest)
Skin: 15 (exercise) vs. 5 (rest)
Stomach: 2 (exercise) vs. 25 (rest)
Volume of Gas Collected (cm³) vs. Temperature (°C):
Trials of varying temperatures and their gas collection amounts across three repetitions.
Verification
Process: Scientists repeat investigations to verify results and eliminate biases or errors.
Sample Size
Definition: The total number of subjects involved in any trial, crucial for statistical validity.
Averages
Arithmetic Mean: Calculated by summing all data points and dividing by the count; useful for large data sets.
Outliers: Values that significantly differ from others can indicate errors and impact the overall mean.
Types of Data
Quantitative Data: Numerical data that can be measured.
Qualitative Data: Descriptive data that characterizes but does not measure.
Types of Errors
Systematic Errors
Definition: Errors that consistently skew measurements in the same direction, affecting accuracy but not reliability.
Causes: Generally stem from poorly calibrated or misused equipment.
Random Errors
Definition: Errors that introduce variability in measurements in a non-consistent manner, impacting reliability but not necessarily the overall accuracy of data.
Ethics in Scientific Research
Definition
Ethics: A set of moral principles guiding compliant behavior in scientific investigations.
Key Principles: List of five core ethical principles that constitute an ethically sound investigation (specific principles omitted).
Learning from Mistakes
Key Note: Acknowledging errors made in research is critical for personal and scientific growth; the key is to learn from these mistakes.
Types of Investigations
Controlled Experiment
One independent variable.
All other variables must be kept constant.
Often employs a double-blind methodology to mitigate bias.
Surveys
Systematic collection, analysis, and interpretation of information about specific areas of study.
Trial and Error
A problem-solving method consisting of multiple attempts until a solution is achieved.
Case Study
In-depth examination of an individual, situation, or small group; can be longitudinal in nature.
Example: Observing a subject with a rare genetic disorder over time.
Caution: Case studies typically do not allow for generalizations.
Longitudinal Studies
Studies conducted over extended periods (e.g., 10 years or more) to capture long-term changes.
Application of Types of Investigations
Phineas Gage's Case: An exploration of behavioral changes following a traumatic brain injury exemplifies case study methodology.
Louis Pasteur's Research: Involvement in controlled experimentation focused on microbial growth under variable conditions.
Antibiotic Efficacy Study: A controlled experiment determining effectiveness of various antibiotics against Streptococcus pyogenes using multiple agar plates.
Community Stress Survey: A longitudinal survey assessing changes in community stress levels over several years through annual questionnaires.