Practical inquiry and IA skills - Chemistry IB Study Notes

Practical Inquiry forms the foundation of scientific investigation in IB Chemistry, encompassing the systematic approach to designing, conducting, and analyzing experiments. The Internal Assessment (IA) is a mandatory component worth 20% of your final grade, requiring you to demonstrate proficiency in experimental design, data collection, analysis, and evaluation.

Key Terms & Definitions:

Independent Variable (IV): The factor you deliberately change or manipulate in an experiment. Only ONE variable should be independent to ensure valid results.

Dependent Variable (DV): The factor you measure or observe that responds to changes in the independent variable. This is your experimental outcome.

Controlled Variables: All factors kept constant throughout the investigation to ensure fair testing. These prevent confounding results.

Precision: How close repeated measurements are to each other, indicating consistency. High precision = low random error.

Accuracy: How close a measurement is to the true or accepted value, indicating correctness. High accuracy = low systematic error.

Uncertainty: The range within which the true value lies, expressed as ± value. Calculate using: Absolute uncertainty (e.g., ±0.01 g) or Percentage uncertainty = (absolute uncertainty / measured value) × 100%

Significant Figures (SF): Digits in a measurement that carry meaningful information. Rules: (1) Non-zero digits are always significant, (2) Zeros between non-zeros are significant, (3) Leading zeros are NOT significant, (4) Trailing zeros after decimal ARE significant.

The IA Criteria include: Research Question (formulation and focus), Exploration (methodology and variables), Analysis (data processing and presentation), Evaluation (conclusions and improvements), and Communication (structure and clarity). Each criterion has specific descriptors that examiners use for marking.

Think of practical inquiry like being a detective solving a mystery—you need a clear question, gather evidence systematically, analyze clues objectively, and draw logical conclusions while acknowledging limitations.

Real-World Application: Pharmaceutical Development

When developing new medications, chemists must investigate how temperature affects reaction rates for drug synthesis. The independent variable might be temperature (20°C, 30°C, 40°C, 50°C), the dependent variable is percentage yield of the product, and controlled variables include reactant concentrations, pH, pressure, and reaction time. Pharmaceutical companies employ the same IA skills you're learning: precise measurements (drugs require exact dosages), uncertainty calculations (quality control tolerances), and thorough evaluation (identifying side effects).

Analogy: Baking a Perfect Cake

Imagine investigating how oven temperature affects cake rise. If you change BOTH temperature AND baking time simultaneously, you won't know which factor caused your cake to be flat—this violates the principle of testing one variable at a time. You must keep flour amount, mixing time, and pan size constant (controlled variables). If your measurement tool (thermometer) is miscalibrated by +10°C, you have systematic error—all readings are consistently wrong. If you estimate temperatures by eye (190°C, 195°C, 185°C for "200°C"), you have random error—readings scatter around the true value.

Environmental Chemistry Connection

Investigating water quality requires measuring pH with ±0.01 precision. If stream pH drops from 7.2 to 6.8, understanding uncertainty helps determine if this change is significant or within measurement error. This practical skill directly applies to monitoring pollution and protecting ecosystems.