The Scientific Method Process

The Scientific Method Process

• Scientific inquiry involves discovering answers to questions about the natural world using a structured approach known as the scientific method.
• The scientific method is not a rigid recipe but a flexible and iterative framework.
• Scientists may revisit previous steps, jump between stages, or start from different points based on new insights or unexpected results.
• The core idea is a systematic way of thinking and investigating, centered on evidence and testing.
• The depiction of the scientific method as a linear sequence (Question → Hypothesis → Experiment → Conclusion) is a simplification for teaching purposes.
• Unexpected results during experimentation might lead directly to new questions or force a revision of the initial hypothesis.
• Observations might continue throughout the process, not just at the beginning.
• Background research might happen concurrently with hypothesis formation or even after initial experiments suggest a new direction.
• Conclusions from one study often serve as the starting point for the next.
• In AI research, exploring complex algorithms and large datasets often leads down unforeseen paths.
• Maintaining rigor and objectivity throughout the investigation is key, regardless of the exact sequence of steps taken.

Scientific Method Steps

• The scientific method typically begins with identifying something you want to understand or solve, triggered by observation, curiosity, or a practical need.
• The question should be clear, focused, and ideally, testable or investigable.
• Vague questions are difficult to tackle scientifically and defining the scope of the problem is important.
• Examples of questions in AI could be improving image classifier accuracy or understanding why a reinforcement learning agent fails in a specific environment.
• An AI team notices their newly developed chatbot frequently misunderstands user requests related to scheduling meetings.

Initial Observation

• The chatbot struggles with complex date/time phrasing.

Recognized Problem

• The chatbot's natural language understanding (NLU) module is not robust enough for scheduling tasks.

Specific Question

• How can the NLU model or training data be modified to improve its accuracy in interpreting scheduling requests by at least 20%?

Make Observations

• Observations involve gathering statements of fact using senses or scientific instruments.

Scientific observations should be:

• Factual: Describe only what happened, not why you think it happened; avoid opinions or interpretations.
• Accurate: Use precise measurements and descriptions whenever possible.
• Detailed: Record relevant details that might be important later (conditions, inputs/outputs).
• Brief: Be concise and to the point while being detailed.
• Non-judgmental: Report objectively without adding personal feelings or conclusions.
• In AI, observations might involve recording specific error messages, noting patterns in model misclassifications, measuring response times, or logging system resource usage during training.

Continuing the chatbot example, detailed observations include:

• User input: 'Can we meet next Tuesday afternoon?' Chatbot response: 'Okay, scheduling for Tuesday 9 AM.' (Factual, detailed: input/output noted)
• User input: 'How about Friday in two weeks?' Chatbot response: 'Sorry, I didn't understand.' (Factual, specific error type)
• When users mention relative times like 'tomorrow' or 'next week', the chatbot correctly identifies the date 95% of the time. (Accurate, uses measurement)
• When users specify 'afternoon' or 'evening', the chatbot defaults to standard business hours (9 AM or 1 PM) 70% of the time. (Accurate, detailed pattern)
• These are facts, not opinions like "The chatbot is bad at times."

Create a Hypothesis

• A hypothesis is a proposed explanation for an observation or answer to a question, is an educated guess based on existing knowledge and observations.

Key characteristics of a scientific hypothesis:

• Testable: Possible to design an experiment or make further observations to potentially prove it wrong (falsifiable).
• Based on Observations/Research: Logically follows from observations or learned information.
• Often in "If-Then" Format: Clearly links a proposed cause (If...) to an expected effect (...Then...). Example: "If [I do this], then [this will happen]."
• In AI, a hypothesis might relate changes in model architecture, training data, or hyperparameters to expected changes in performance metrics.

Continuing the chatbot example

Based on the observations that the chatbot struggles with complex and relative time phrasing:

• The hypothesis is "If we augment the training dataset with diverse examples of natural language scheduling requests, including relative times and times of day, then the chatbot’s accuracy in correctly interpreting these requests will increase significantly compared to the current baseline model."
• Testable, based on observations, and implies a prediction (accuracy will increase).

Experimentation

• Experimentation is the core step where the hypothesis is actively tested.
• The goal is to gather data that will either support or refute the hypothesis.
• A key aspect is designing controlled experiments.
• Controlled experiments: experiments where the researcher keeps all conditions constant except for one factor they intentionally change to observe its effect, reliably linking cause and effect.

A well-designed controlled experiment typically includes:

• Control Group: The group that does not receive the experimental treatment or change, serving as a baseline or standard for comparison, representing "normal" conditions.
• Experimental Group: Receives the experimental treatment or change being tested (the independent variable).
• Independent Variable: The single factor that the scientist intentionally changes or manipulates between groups, testing the effect of. (The "If" part of the hypothesis).
• Dependent Variable: Measured or observed to see if it changes as a result of manipulating the independent variable; what you expect to be affected. (The "Then" part of the hypothesis).
• Constants: All other factors or conditions kept the same for both groups, ensuring any observed difference in the dependent variable is likely due to the independent variable.
• In AI, this often translates to A/B testing, where Model A (control) is compared against Model B (experimental) with one specific change (different training data, hyperparameter setting, or architecture modification).

To test the hypothesis about augmented data:

• Control Group: The original chatbot model, trained on the original dataset.
• Experimental Group: An identical chatbot model architecture, trained on the original dataset plus the newly added diverse scheduling examples.
• Independent Variable: The training dataset (Original vs. Augmented).
• Dependent Variable: The accuracy of the chatbot in interpreting scheduling requests (measured on a standardized, unseen test set containing diverse requests); precision, recall, F1-score could also be dependent variables.
• Constants: Model architecture, training algorithm, number of training epochs, hardware used for training, the test dataset used for evaluation, other hyperparameters (like learning rate, or batch size).
• The team trains both models and evaluates their performance on the same test set.

Data Analysis

• Data analysis is the process of inspecting, cleaning, transforming, and modeling the data collected during experimentation to discover useful information and draw conclusions.
• Data analysis is distinct from data collection, which simply involves gathering raw numbers or observations.
• Analysis involves making sense of the data, often using statistical methods or visualization tools.

Examples include:

• Calculating averages, percentages, or rates of change.
• Creating graphs or charts (bar charts, line graphs, or scatter plots) to visualize trends or comparisons.
• Applying statistical tests to determine if observed differences are statistically significant (unlikely due to random chance).
• In AI, calculating metrics like accuracy, precision, recall, F1-score, confusion matrices, and plotting learning curves.
• The goal is to objectively summarize the evidence gathered in relation to the hypothesis.

Analyzing chatbot models

• The AI team analyzes the performance data from the control and experimental chatbot models:
• Control Model Accuracy: 65% on the scheduling test set.
• Experimental Model Accuracy: 88% on the scheduling test set.
• Analysis: They calculate the percentage point increase (23 points).
• A bar chart comparing accuracies could be created, analyzing specific error types reduced in experimental model (misinterpretation of 'afternoon' dropped by 50%).
• Statistical tests might confirm the improvement is significant.
• This analysis transforms raw accuracy scores into interpretable insights.

Conclusions

• The conclusion summarizes the findings of the experiment and directly addresses the hypothesis.

Key elements include:

• Restate the Hypothesis (briefly).
• State whether the data supports or refutes the hypothesis, using clear language.
• "The data supports the hypothesis that..." or "The data refutes the hypothesis that...".
• Avoid saying the hypothesis is "proven" because science deals in evidence and support, not absolute proof.
• Provide Key Supporting Data: Summarize the main findings from the data analysis that justify your conclusion (e.g., "Accuracy increased from X% to Y%").
• Discuss Implications/Next Steps: What does this result mean? If the hypothesis was rejected, it might need to be revised based on the new data, or discarded entirely leading to new hypotheses. If verified, it strengthens the explanation.
• Conclusions should be based only on the data collected in the experiment.

Conclusion in the chatbot example

• Based on the analysis - The hypothesis stated that augmenting the training data with diverse scheduling examples would significantly increase chatbot accuracy.
• The experimental data supports this hypothesis.
• The model trained on augmented data achieved 88% accuracy on the test set, improving over the control model's 65% accuracy by 23 percentage points.
• Incorporating more varied natural language examples related to time and scheduling is effective for improving the chatbot's NLU performance.
• Next steps could involve further refining the augmented dataset, testing the model with real users, or exploring architectural changes in addition to data augmentation.

Scientific Knowledge Hierarchy

• Scientific knowledge isn't just a collection of random facts but is organized into a hierarchy based on the breadth of explanation and the amount of supporting evidence.
• Understanding this hierarchy helps distinguish between different types of scientific claims.
• Two key levels are Principles/Laws and Theories.

Principles or Laws

• A scientific Principle or Law describes a specific relationship or consistent pattern observed in nature (or in a defined system like computation) that holds true under specified conditions and has not been contradicted by empirical testing.

Laws and Principles

• They often describe what happens, sometimes mathematically (e.g., F=ma, Ohm's Law V=IR).
• They are generally narrow in scope compared to theories.
• They are considered factual descriptions of specific phenomena based on repeated, consistent evidence.
• Examples of laws and principles: Ohm's Law states a specific relationship between voltage (V), current (I), and resistance (R) in an electrical circuit.
• Further example of principles - In computer science and AI, certain principles guide design, such as principles of algorithm efficiency or fundamental concepts in probability theory that underpin machine learning.

Theories

• A scientific Theory is a broad, well-substantiated explanation for some aspect of the natural world (or a complex domain).
• It goes beyond describing what happens (like a law) to explain why or how it happens.

Theory Key points

• Links Many Principles/Laws: Theories often incorporate and synthesize multiple laws, hypotheses, and observations into a coherent framework.
• Explanatory Power: Their value lies in their ability to explain a wide range of phenomena.
• Not Guesses: In science, a theory is not just a guess or hunch since it represents a pinnacle of scientific understanding, supported by a vast body of converging evidence from many independent lines of inquiry.
• Testable and Falsifiable: Like hypotheses, theories must make predictions that can be tested, and they are always subject to revision or even rejection if significant new contradictory evidence emerges.
• Examples of theories - Acceptable if they are the best available explanation fitting the current evidence include the Theory of Evolution by Natural Selection, Germ Theory of Disease, and the Big Bang Theory.
• In AI, foundational concepts like Statistical Learning Theory provide a mathematical framework explaining why and how certain machine learning algorithms work and generalize from data.
• Theory of Evolution by Natural Selection: This explains the diversity of life on Earth with principles from genetics and paleontology
• Describes how species change over time with variation and reproduction.
• Supported by massive evidence, the theory makes testable predictions (fossil records, and the relationships between organisms).