Chapter 1 | Scientific Investigation

Section 1: Hypothesis Testing

1. Define hypothesis testing
   A process to test an assumption (hypothesis) about a population using data.

2. Understand the types of hypotheses

   • Null (H₀): No effect/difference.

   • Alternative (H₁): Shows effect/difference.

3. Outline the steps in hypothesis testing

   1. State H₀ and H₁

   2. Choose significance level (α)

   3. Select test statistic

   4. Determine critical region

   5. Collect data and compute statistic

   6. Compare and conclude (reject or fail to reject H₀)

4. Formulate a null hypothesis
   A default assumption (e.g., "The medicine has no effect").

5. Formulate an alternative hypothesis
   A statement that contradicts H₀ (e.g., "The medicine improves health").

6. Select a significance level

   • Common α levels: 0.05, 0.01

   • Represents the probability of Type I error (false positive)

7. Choose an appropriate test statistic

   • Depends on data type (e.g., t-test for means, chi-square for categories)

Section 2: Experimental Design

1. Define the problem
   State clearly what is being tested or investigated.

2. Formulate a hypothesis
   Create testable H₀ and H₁ based on the problem.

3. Design an experiment
   Plan method: variables, controls, and procedure.

4. Conduct the experiment
   Follow the procedure; control bias and maintain consistency.

5. Analyze the data
   Use statistical tools to interpret results.

6. Draw conclusions
   Decide whether to reject or fail to reject H₀ based on data.

Section 3: Data Analysis

1. Define data analysis and its importance
   Organizing and interpreting data to extract meaning and support conclusions.

2. Identify data sources and types

   • Sources: surveys, sensors, experiments

   • Types: quantitative (numbers), qualitative (descriptions)

3. Learn data collection methods

   • Surveys, observations, experiments

   • Important: reliability and validity

4. Apply data preprocessing techniques

   • Cleaning, normalization, handling missing data

5. Perform exploratory data analysis (EDA)

   • Use graphs, summaries to understand patterns/trends

6. Implement statistical analysis

   • Inferential methods: t-tests, correlation, regression

7. Use visualization tools for data interpretation

   • Graphs, charts (histograms, scatterplots, boxplots)

8. Develop data-driven decision-making strategies

   • Use insights to support logical and strategic decisions

9. Address data quality issues

   • Check for bias, outliers, duplicates, and missing entries

10. Understand data security and privacy

• Protect sensitive information; follow legal/ethical guidelines