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
- The ocean's pH is lowering due to acid rain.
- 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 → (sulfuric acid).
- Nitric acid formation: Nitrogen dioxide + water → (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
- Formulate a question.
- Define variables.
- Obtain permissions.
- Conduct experiments.
- Analyze data:
- Use statistical methods to draw relationships.
- Discuss findings and interpretations.
- 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.