Essential Biological Research Terms and Statistical Methods

Scientific Inquiry and Experimental Design

• Hypothesis: A proposed, testable biological explanation for an observation that is established before testing.
• Prediction: An expected outcome, often phrased as an "If... then..." statement, but lacks the underlying explanatory "why" or "how".
• Claim: A definitive concluding statement made after analyzing experimental data, graphs, and figures that is directly supported by evidence.
• Null Hypothesis: A statement predicting that there is no statistically significant difference between your experimental groups.

Variables and Controls

• Independent Variable: The specific factor you purposefully manipulate or test.
• Dependent Variable: The specific data you are collecting and measuring.
• Positive Control: A trial given a treatment with a known, expected outcome to verify the experimental system is functioning properly.
• Negative Control: A trial given no treatment or a placebo to establish a reliable baseline.
• Sample Size & Trials: The number of subjects or repeats in a study; larger sample sizes generate data sets that are highly representative of the true mean.

Statistical Analysis and Interpretation

• Normal Distribution: A bell-shaped data curve where values are clustered symmetrically around the sample mean.
• Standard Deviation: Represents how far individual data points spread or deviate from the sample mean.
• Standard Error of the Mean (SEM): Evaluates the precision of your sample mean.
• 95% Confidence Intervals & Error Bars: Used to construct error bars to visualize statistical significance and identify which sample set has the most variability (the widest bars).
• Error Bar Overlap Rule: If error bars overlap between samples, there is no significant difference (fail to reject the null hypothesis). If they do not overlap, there is a significant difference (reject the null hypothesis).
• Chi-Square Decision Rule: If your calculated Chi-Square value is greater than the critical value, you reject the null hypothesis because the difference is statistically significant. If it is lower, you fail to reject it.

Calculations and Formulas

• Standard Deviation (SD): You do not need to calculate this on the test, but you must know it represents the spread of your data.
• Standard Error of the Mean (SEM): Identify the standard deviation (s) and total sample size (n), find the square root of the sample size (√n), and divide the standard deviation by that square root.
• 95% Confidence Intervals (Error Bars): Multiply your SEM by 2, then add this number to your sample mean for the top boundary and subtract it for the bottom boundary.
• Chi-Square Analysis (χ²): Calculate expected values (E), subtract expected from observed (O - E), square that result, divide by expected, and add up the final values for all categories.