Scientific Method

Understanding Scientific Variables

Types of Variables

• Types of variables used by scientists
  • Variables vary in significance based on the research question.
  • Types include independent, dependent, and confounding variables.

Independent Variables

• Definition: Independent variables are those variables that are being tested or studied by scientists.
• Characteristics:
  • Data collected can be graphed.
  • Identified on the x-axis of a graph.

Dependent Variables

• Definition: Dependent variables are the ones affected by the independent variables.
• Characteristics:
  • Found on the y-axis of a graph.

Graph Interpretation

• Example graph: Ocean pH over time.
  • X-axis: Time
  • Y-axis: pH
• Identifying Variables from the Graph:
  • Independent Variable: Time (on the x-axis).
  • Dependent Variable: pH (on the y-axis).

Specific Data from the Graph

• Gray Line: Represents the tropics.
• Blue Line: Represents the Arctic.
• Orange Line: Represents the Southern Ocean.
• Key Observations:
  • The Southern Ocean shows the highest pH around the year 2010.
  • The tropics exhibit the lowest pH observed in 2010.
  • By 2050, the Arctic will experience the lowest pH levels.

Hypotheses Related to Ocean pH

1. The ocean's pH is lowering due to acid rain.
2. The ocean's pH is lowering due to increases in atmospheric carbon dioxide levels.

Emissions and Chemical Reactions

• Examining emissions: Sulfur dioxide and nitrogen oxides.
• Chemical reactions:
  • Sulfuric acid formation: Sulfur dioxide + water → $H<em>2SO</em>4$ (sulfuric acid).
  • Nitric acid formation: Nitrogen dioxide + water → $HNO_3$ (nitric acid).

Emissions Analysis (1990-2015)

• In terms of annual emissions:
  • Sulfur dioxide emits more than nitrogen oxides.
  • Represents a clear trend with sulfur dioxide (dark blue) being more prevalent than nitrogen oxides (green).

Carbon Dioxide Trends in Hawaii

• Observations (1990-2015):
  • CO2 levels in Hawaii increased, while sulfur dioxide and nitrogen oxides decreased.
• Contributing Factors:
  • Natural emissions from active volcanoes influence CO2 levels.

Confounding Variables

• Definition: Confounding variables are external variables that might affect the dependent variable other than the independent variable.
• Issues with confounding variables:
  • Introduce unwanted variation while studying hypotheses.
  • Examples of bias introduction:
  • Sampling only during known acid rain events.
  • Collecting samples from a single area or at different times of the year.

Controlling Confounding Variables

• Use of blind studies and randomization to mitigate bias in studies.
• Control groups and experimental groups must be carefully defined in scientific experiments.
• Example:
  • A study testing the effect of vitamin C on the common cold would have:
  • Experimental group (receives orange juice).
  • Control group (receives no orange juice).

Types of Data

Quantitative Data

• Definition: Data consisting of numerical values that can be measured and analyzed statistically.
• Examples include measuring pH and determining relationships with marine life, such as shellfish.

Qualitative Data

• Definition: Data gathered from surveys, interviews, or focus groups, often represented in words or images rather than numbers.
• Example of qualitative research:
  • Surveying individuals on the effects of music on studying.
  • Gathering insights based on responses regarding music preferences while studying.

Mixed Methods Approach

• Combination of both qualitative and quantitative data can be used for comprehensive analysis.

Ethical Considerations in Scientific Research

• Institutional Review Board mandates oversight for human-related studies to ensure ethical conduct.
• Historical example: Tuskegee Syphilis Study
  • Background: African American men were left untreated for syphilis without their informed consent from 1932 to 1972, despite available treatment.
• Resulted in significant ethical reforms in research policies to protect subjects.

Steps in the Scientific Method

1. Formulate a question.
2. Define variables.
3. Obtain permissions.
4. Conduct experiments.
5. Analyze data:
   • Use statistical methods to draw relationships.
6. Discuss findings and interpretations.
7. Draw conclusions:
   • State whether the hypothesis was supported rather than stating it was proven.

Central Tendency Analysis

• Methods to analyze data include determining the mean, median, or mode.
• Example of analysis: Examining student height data and creating histograms.

Data Presentation

• Effective usage of graphs to represent data trends clearly.
• Significance of visually summarizing results (e.g., displaying emissions trends of sulfur and nitrogen oxides).

Conclusion Writing

• Reinforcement of hypotheses based on collected data.
• Example: Observing a car’s failure to start leads to hypothesizing that the battery is dead; if the new battery solves the issue, the hypothesis was supported but not definitively proven, allowing room for alternative explanations.