Science inquiry skills (Slides) UHS 2025 -

Scientific methods enable systematic investigation to obtain measurable evidence.
Key components of an experiment include:
- A hypothesis or inquiry question
- Types of variables:
  - Dependent Variable
  - Independent Variable
  - Factors Held Constant: Explanation of control and uncontrolled factors
- Materials required
- Methodology with justification
- Types and amounts of data to be collected
- Identification of ethical and safety considerations
Critical evaluation of procedures and data to determine meaningful results:
- Identify sources of uncertainty:
  - Random errors
  - Systematic errors
- Evaluate reliability, accuracy, and validity of results through:
  - Sample size
  - Random error
  - Systematic error

Definition: Factors kept constant to prevent influence on results.
Importance: Ensures isolation of the independent variable’s effect, improves accuracy, ensures reliable findings.

Definition: Unintended factors that can influence outcomes if not controlled.
Importance: Can introduce bias, reduce accuracy, and complicate the determination of true effects of the independent variable.

Definition: A testable prediction involving independent and dependent variables.
Note: Should not attempt to explain phenomena in the hypothesis.
Examples:
- Drinking more water improves energy levels.
- Higher protein intake leads to fullness and reduced calorie intake.
- Consuming high-sugar foods decreases cognitive focus within an hour.

Mistakes: Easily avoidable errors, e.g., misreading a scale or poorly conducted procedures.
Note: Mistakes should not be discussed in practical reports.

Definition: Caused by factors that randomly affect raw data; unpredictable and uncontrollable by nature.
Example: Inconsistent measurements (timing, temperature, etc.).
Effect: Reduces precision of results, reflected in scatter of data.

Definition: Closeness of two or more measurements.
Indicators: Lower scatter around the mean indicates higher precision, caused by random errors.

Definition: Extent to which an experiment yields consistent results under the same conditions.
Achieved by minimizing random errors and mistakes; promotes using larger sample sizes.

Definition: Number of samples or observations in an investigation.
Example: 6 replicates for testing light intensity on plant growth yields a sample size of 6.

Increasing sample size reduces the effect of random errors, leading to more reliable results.
Note: Increasing sample size does not eliminate random errors.

Definition: Present when measured values deviate consistently from the true value.
Impact: Reduces accuracy (averaging does not correct this).
Examples: Incorrectly calibrated equipment, contaminants affecting measurements.

Validity: Degree to which an investigation tests its intended measure.
Accuracy: How close the experimental results are to the true value; systematic errors must be identified and minimized.

Student A:
- pH Readings: 4.3, 5.0, 4.9, 4.4, 4.7
- Mean: 4.6
Student B:
- pH Readings: 4.5, 4.6, 4.6, 4.5, 4.5
- Mean: 4.5
Precision: Student B has less scatter; hence, their results are more precise.
Accuracy: Cannot be determined without a true value.
Average calculation improves reliability by increasing sample size, reducing random error effects.

Summary of scientific method components, importance of hypothesis formulation, clarity on types of variables, critical evaluation of results in terms of reliability, accuracy, and validity, including consideration of uncertainties.