BIOL 1106 General Biology Laboratory Notes

BIOL 1106 General Biology Laboratory Notes

Scientific Method: The Process of Science

What is Science?

• Science is defined as a process, emphasizing it as an activity rather than a static body of knowledge.

• It represents a way of learning and knowing about the natural world.

• Involves posing and answering questions relevant to the observable universe.

• Based on facts that are gathered through repeated observations and/or experimentation.

• Empirical data, which primarily consists of numbers, plays a crucial role in scientific methodology.

• The process allows for objectivity; personal judgments and biases are not accommodated in scientific practice.

6 Steps of the Scientific Method

1. Observation

2. Hypothesis

3. Experiment

4. Data Analysis

5. Conclusion

   • Decide to support, reject, or revise the hypothesis.

6. Scientific Theory

Step 1: Observation

• Involves firsthand noticing and experiencing phenomena through the five senses:

  • Seeing

  • Hearing

  • Smelling

  • Touching

  • Tasting (Note: generally not applicable in laboratory settings)

• Creates curiosity or wonder about potential cause and effect relationships.

• Important to discern between systematic causes versus random patterns.

Step 2: Hypothesis

• Formulate a reasonable, testable hypothesis based on existing knowledge.

  • A hypothesis is characterized as an untested explanation that seeks to clarify initial observations.

  • States a possible cause-and-effect relationship.

• Must be empirically testable, meaning the results can be measured and expressed numerically.

• Empirical Definition: Observable facts aiding in quantifiable analysis.

• The hypothesis should exclude supernatural elements.

Example of Hypothesis:

• Hypothesis: "Ulcers are caused by bacteria."

  • This allows for the design of an experiment to test this relationship.

• Predictions can be framed as an if/then statement.

  • Example: "If I give ulcer patients an antibiotic, then the ulcers should disappear."

• There are two types of hypotheses:

  1. Alternate Hypothesis: Presumes the treatment has an effect (e.g., antibiotic leads to ulcer disappearance).

  2. Null Hypothesis: Presumes no effect (e.g., antibiotic does not lead to ulcer disappearance).

Step 3: Experiment

• The goal is to test the hypothesis by observing potential cause and effect relationships.

• Key variables include:

  • Independent Variable: The potential cause; manipulated by the experimenter.

  • Common independent variables: time, pH, temperature, drug treatment regimens.

  • This variable is presented on the X-axis of graphs.

  • Dependent Variable: The potential effect; its value responds to the independent variable’s change.

  • Measured by the experimenter after adjusting the independent variable; displayed on the Y-axis.

• It is crucial to assess the impact of only the one testing variable; control for others that could confound results.

Example Scenario in an Experiment:

• Experimental Group: Receives treatment (e.g., antibiotics).

• Control Group: Receives standard or no treatment, acting as a baseline for comparison.

• Both groups must be identical in composition except for the treatment received.

Step 4: Running the Experiment

• Execute the main experiment along with any necessary control experiments in parallel.

• Collect actual raw data during this phase.

Step 5: Data Analysis

• Summarize raw data using tables or graphs to visualize results.

• Statistical calculations to determine:

  • Mean (average)

  • Variance

  • Standard deviation of the data set.

• Statistical methods are applied to compare experimental and control group data to ascertain if the independent variable had a significant effect.

• For null hypotheses, the intent is to find no difference between datasets.

Step 6: Conclusion

• Assess whether actual data supports the original hypothesis by comparing it with predicted outcomes.

• If predicted matches actual data, conclude that the hypothesis is supported (or "fail to reject").

  • Important to note that hypotheses cannot be conclusively proven.

• If the predicted data does not align with actual results, the hypothesis is rejected, leading to necessary revisions and re-testing.

• Preference exists for testing null hypotheses within drug trials, as rejection determines definitive findings against a hypothesis.

Step 7: Scientific Theory

• Broad hypotheses that endure rigorous testing and are repeatedly supported by evidence become scientific theories, widely accepted in the scientific community.

• Scientific Theory Definition:

  • Represents hypotheses thoroughly tested through a multitude of related experiments.

• Examples of Scientific Theories:

  • Theory of Gravity

  • Theory of Relativity

  • Cell Theory

  • Theory of Evolution

Importance of Measurements

• Always include units when reporting measurements (e.g., 2 cm, 23 °C, not merely 2, 23).

Statistical Concepts

Calculating the Mean

• The Mean is calculated as:
  $ext{Mean} = rac{ ext{Sum of all measurements}}{ ext{Number of measurements}}$

• Example Calculations:

  • Data set 1: 2 mm, 3 mm, 4 mm → Mean = $rac{(2 + 3 + 4)}{3} = 3 ext{ mm}$

  • Data set 2: 1 mm, 3 mm, and 5 mm → Mean = $rac{(1 + 3 + 5)}{3} = 3 ext{ mm}$

Calculating Variance

• The variance measures the spread of individual data points from the mean:

• Variance Formula:
  $ext{Variance} = rac{ ext{Sum of squared deviations}}{N-1}$

• Each deviation is computed as:

  • $x<em>i - ext{Mean}$ where $xi$ is each individual data point.

  • Greater spread equals higher variance.

Example Calculations of Variance

• 
  

• Data Set 1:
  

  Data Point	Mean	Deviation	Deviation²
  2 mm	3 mm	-1 mm	1 mm²
  3 mm	3 mm	0 mm	0 mm²
  4 mm	3 mm	1 mm	1 mm²
  Variance = $rac{2 ext{ mm}^2}{3 - 1} = 1 ext{ mm}^2$

  • 
    

  • Data Set 2:
    

    Data Point	Mean	Deviation	Deviation²
    1 mm	3 mm	-2 mm	4 mm²
    3 mm	3 mm	0 mm	0 mm²
    5 mm	3 mm	2 mm	4 mm²
    Variance = $rac{8 ext{ mm}^2}{3 - 1} = 4 ext{ mm}^2$

    Standard Deviation (SD)

    • The standard deviation (SD) is calculated as:
      $ext{SD} = ext{sqrt(Variance)}$

    • It is crucial to report the SD alongside the mean:

      • Data Set 1: Mean = 3 mm, SD = 1 mm

      • Data Set 2: Mean = 3 mm, SD = 2 mm

    Significance Testing with Standard Deviation

    • Means are compared with significance determined by the overlap of means ± ½ SD.

    • Two means are statistically significantly different if they do not overlap when calculating the ranges:

      • Example:

      • Data Set 3: Mean = 23 ± 5 g (Range: 20.5 g to 25.5 g)

      • Data Set 4: Mean = 20 ± 4 g (Range: 18 g to 22 g)

    • Since ranges overlap, they are not significantly different.

    Proper Rounding of Numbers

    • To round numbers in this context, round up if the next digit is 5 or higher:

      • Example: 89.49 → 89; 89.50 → 90

    Identifying Non-Scientific Evidence

    • Many forms of non-scientific evidence are present in commonsense thinking:

      • Mere opinions and beliefs

      • Consensus views and authority figures

      • Flawed reasoning such as cherry-picking and informal logic

    Spotting Bad Science

    12 points essential for evaluating and spotting bad science include:

    1. Sensationalized Headlines - Overly simplified or misrepresentative headlines.

    2. Misinterpreted Results - Distorted research results for captivating narratives.

    3. Unrepresentative Samples - Samples that do not reflect the general population may lead to biased conclusions.

    4. No Control Group - Essential in clinical trials to compare test subjects accurately.

    5. Correlation vs. Causation - Not all correlations imply that one variable causes another.

    6. Unsupported Conclusions - Speculative statements requiring further evidence.

    7. Problems with Sample Size - Smaller samples yield lower confidence in results.

    8. No Blind Testing - Bias prevention through blinding test and control groups.

    9. Selective Reporting of Data - Also known as cherry-picking, where supporting data is favored.

    10. Unreplicable Results - Results should be reproducible by outside researchers.

    11. Non-Peer Reviewed Material - Peer review is crucial for validating research quality.

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