Difficulty: Intermediate | Prerequisites: Basic microbiology concepts, colony counting, intro to statistics (t-tests, p-values)
This lab sits within the broader BIO 101 unit on microbiology and evolution, specifically how natural selection drives antibiotic resistance in bacterial populations. The experiment compares tetracycline-resistant bacteria in soils from state/national parks versus public urban parks, testing whether human activity correlates with higher resistance levels. You should already be comfortable with what antibiotics do at the cellular level and how selective pressure works. If you are not, review your notes on natural selection and bacterial reproduction first.
Researchers collected soil from state/national parks and public urban parks, plated bacteria on tetracycline-laced agar at two concentrations (3 µg/mL and 30 µg/mL), and compared the percentage of resistant colonies. State park soils showed significantly higher low-level tetracycline resistance than public park soils, but at the higher antibiotic dose the difference disappeared. The key lesson: resistance prevalence depends on both the environment sampled and the selective pressure (antibiotic concentration) you test at.
Antibiotic resistance
The ability of bacteria to survive and reproduce in the presence of an antibiotic that would normally kill them or stop their growth. Think of it as bacteria that have evolved a defence against a specific drug.
Tetracycline (tet)
A broad-spectrum antibiotic that works by blocking bacterial ribosomes from making proteins. It is commonly used in human medicine and agriculture, which is why it turns up so often in resistance studies.
%tetR (percent tetracycline-resistant)
The percentage of bacterial colonies on an agar plate that can grow in the presence of tetracycline. In simple terms, it is the fraction of your soil bacteria that shrug off the drug.
CFU/g (colony-forming units per gram)
A count of viable (living, able to reproduce) bacterial cells per gram of soil. Each colony on a plate grew from one original cell, so the colony count equals the cell count. Think of it as a head-count of living bacteria in a gram of dirt.
Null hypothesis (H₀)
The default assumption that there is no difference between the two groups being compared. In this lab: "There is no difference in antibiotic resistance between state park soil and public park soil." You try to reject it with data.
t-test (Student's t-test)
A statistical test that compares the means of two groups and tells you whether the difference is likely real or just due to chance. A large t-value (far from zero) combined with a small p-value suggests a real difference.
p-value
The probability of seeing a result at least as extreme as yours if the null hypothesis were true. A p-value below 0.05 is the conventional threshold for "statistically significant." In simple terms, a tiny p-value means your result is unlikely to be a fluke.
95% confidence interval (95% CI)
A range of values within which you can be 95% confident the true population mean falls. A narrow CI means your estimate is precise; a wide one means you are less certain.
Degrees of freedom (df)
The number of independent values that can vary in your dataset after you have calculated a statistic. For a two-sample t-test with n₁ + n₂ observations, df = n₁ + n₂ - 2.
Selective pressure
An environmental factor (here, the presence of an antibiotic) that favours organisms with a particular trait. Higher antibiotic concentration = stronger selective pressure, meaning only bacteria with robust resistance mechanisms survive.
Research hypothesis: State park soils will show less antibiotic resistance than public (urban) park soils, because public parks are closer to human antibiotic use (hospitals, agriculture, household waste).
Null hypothesis (H₀): There is no difference in antibiotic resistance between state park and public park soils.
Soil sources: Three state/national parks and three public urban parks.
Method: Soil samples were plated on agar containing tetracycline at two concentrations:
Low dose: 3 µg/mL
High dose: 30 µg/mL
The percentage of colonies that grew (%tetR) was recorded for each site and concentration.
State parks mean %tetR: 46.07 (95% CI: 2.19 × 10¹, n = 3)
Public parks mean %tetR: 12.53 (95% CI: 1.80 × 10¹, n = 3)
t-value: -5.09, df = 4, one-tailed p = 3.52 × 10⁻³
Interpretation: The difference is statistically significant (p < 0.05). The null hypothesis is rejected at this concentration. State park soil had roughly 3.7 times the low-level resistance of public park soil.
State parks mean %tetR: 3.15 (95% CI: 5.48, n = 3)
Public parks mean %tetR: 2.29 (95% CI: 4.25, n = 3)
t-value: 0.53, df = 4, one-tailed p = 3.12 × 10⁻¹
Interpretation: The difference is not statistically significant (p = 0.31, well above 0.05). We fail to reject the null hypothesis at this concentration. Both environments had similarly low percentages of highly resistant bacteria.
At the low dose, many soil bacteria can tolerate a small amount of tetracycline, and state parks had far more of these.
At the high dose, very few bacteria in either environment could survive, suggesting that strong, clinically relevant resistance is rare in both settings.
The dramatic drop from ~46% to ~3% (state parks) and ~13% to ~2% (public parks) shows that most "resistant" colonies at 3 µg/mL only have low-level resistance, not the robust resistance you would see in a hospital superbug.
Percentage tetracycline-resistant (%tetR)
%tetR = (CFUs on tet plate / CFUs on control plate) × 100
This gives you the proportion of the total bacterial population that can grow in the presence of the antibiotic.
Two-sample t-test (unpaired)
t = (mean₁ - mean₂) / √(s₁²/n₁ + s₂²/n₂)
Where s² is the sample variance and n is the sample size for each group. The result is compared against the t-distribution with df = n₁ + n₂ - 2.
Interpreting the t-test output
A large absolute t-value (far from zero) suggests the means are well separated relative to the variability in the data.
The p-value tells you how likely you are to see that t-value (or larger) by chance alone.
p < 0.05: reject H₀ (significant difference).
p ≥ 0.05: fail to reject H₀ (no significant difference detected).
Soil is a natural reservoir for antibiotic resistance genes. Monitoring resistance levels in different environments helps public-health researchers track how resistance spreads from farms and cities into wild ecosystems. This matters because resistance genes can transfer between soil bacteria and human pathogens through a process called horizontal gene transfer.
The same plating-and-counting technique used here is standard in environmental microbiology labs, water-quality testing, and food-safety inspections. If you go on to work in public health or environmental science, you will use CFU counts and resistance screening routinely.
"Public parks should have more resistance because they are near people." The data actually showed the opposite at the low tetracycline dose: state park soils had higher %tetR. Resistance in soil bacteria is not solely driven by proximity to hospitals or pharmacies; natural soil bacteria carry ancient resistance genes that pre-date clinical antibiotic use.
"A higher percentage of resistant bacteria means the soil is more dangerous." Not necessarily. %tetR tells you about the proportion of resistant colonies, not about the total bacterial load or whether those bacteria are pathogenic. High %tetR in park soil does not equal a health hazard.
"Failing to reject the null hypothesis means the groups are the same." It means you lack sufficient evidence to declare them different. With only n = 3 per group, statistical power is low, so a non-significant result at 30 µg/mL could reflect a real-but-small difference that the sample size was too small to detect.
"A significant p-value proves causation." The t-test tells you the difference is unlikely to be chance alone. It does not tell you why the difference exists. Correlation and experimental design determine whether you can infer causation.
⚠️ Know the difference between "reject H₀" and "fail to reject H₀." This lab gives you one of each: rejected at 3 µg/mL, failed to reject at 30 µg/mL. Be ready to explain both.
⚠️ Be able to read a t-test output: identify the t-value, df, p-value, and confidence interval, and state what they mean in plain English.
⚠️ Understand that the same two populations can show a significant difference at one antibiotic concentration and no significant difference at another. The concentration you test at changes the question you are asking.
⚠️ Do not confuse statistical significance with biological importance. A p-value tells you about the reliability of the result, not about how large or meaningful the effect is.
⚠️ The lab report labels the state parks as "control" and public parks as "treatment." Be prepared to justify why (state parks have less direct human antibiotic input, so they serve as the baseline).
True or false: A p-value of 0.31 means the null hypothesis is true. Answer: False. It means you do not have enough evidence to reject it.
Fill in the blank: %tetR is calculated by dividing ______ on the antibiotic plate by ______ on the control plate, then multiplying by 100. Answer: CFUs; CFUs.
True or false: The experiment found a statistically significant difference in resistance at 30 µg/mL tetracycline. Answer: False. The p-value was 0.31, well above 0.05.
Fill in the blank: In a two-sample t-test with 3 samples in each group, degrees of freedom = ______. Answer: 4 (3 + 3 - 2).
True or false: State park soils had a higher %tetR than public park soils at the low tetracycline dose. Answer: True (46.07 vs 12.53).
Q: Why were two different tetracycline concentrations used in this experiment?
A: To distinguish between low-level resistance (bacteria that tolerate a small dose) and high-level resistance (bacteria that survive a clinically relevant dose). Testing at only one concentration would miss this distinction.
Q: The state park %tetR was 46.07 at 3 µg/mL but only 3.15 at 30 µg/mL. What does this drop tell you about the resistant population?
A: Most bacteria in state park soil carry only weak resistance mechanisms that break down at higher antibiotic concentrations. The small fraction (~3%) that survived 30 µg/mL likely possess stronger resistance genes, such as efflux pumps or ribosomal protection proteins.
Q: The researchers labelled state parks as the "control" group. Justify this choice.
A: State parks are more remote from concentrated human antibiotic use (urban runoff, agricultural waste, sewage), so they represent a baseline level of naturally occurring resistance. Public parks, being in urban areas with greater antibiotic exposure, serve as the treatment (test) group.
Q: A classmate says "the p-value at 30 µg/mL was 0.31, so there is definitely no difference between the parks at that dose." What is wrong with this statement?
A: "Fail to reject H₀" does not mean "H₀ is true." With only n = 3 per group, the test has low statistical power. There may be a real but small difference that the sample size was too small to detect.
Q: If you wanted to strengthen the conclusions of this study, what single change to the experimental design would help most?
A: Increase the sample size (more parks, more soil replicates per park). Larger n increases statistical power and narrows confidence intervals, making it easier to detect real differences and more confident in non-significant results.
This lab connects directly to the evolution unit: antibiotic resistance is one of the clearest real-time examples of natural selection. Bacteria with resistance genes survive and reproduce; those without die. Over generations the resistant proportion grows, which is exactly the mechanism Darwin described, just on a microbial timescale.
The statistical methods here (t-tests, confidence intervals, p-values) reappear throughout BIO 101 and any lab science course. Getting comfortable reading a t-test output now will save you time in ecology, genetics, and physiology labs later.
Horizontal gene transfer, the process by which resistance genes move between unrelated bacteria, ties this topic to the genetics and molecular biology sections of the course. If you covered plasmids, conjugation, or transformation, those mechanisms are how resistance spreads through soil and clinical bacterial populations.
Antibiotic resistance, tetracycline resistance, %tetR, tetR gene, soil microbiology, environmental microbiology, CFU colony forming units, agar plating, selective media, two-sample t-test, unpaired t-test, Student's t-test, p-value, null hypothesis, statistical significance, confidence interval, degrees of freedom, selective pressure, natural selection in bacteria, horizontal gene transfer, plasmid-mediated resistance, resistome, AMR antimicrobial resistance, BIO 101, introductory biology lab, University of Washington biology, park soil bacteria, environmental resistance screening