Lab Midterm

LAB 1 — EXPERIMENTAL DESIGN & SCIENTIFIC MEASUREMENT

LAB 1 — EXPERIMENTAL DESIGN & SCIENTIFIC MEASUREMENT

Scientific Method

• Make an observation

• Ask a question

• Conduct a literature review

• Form a hypothesis

• Design an experiment

• Run the experiment

• Analyze data

• Evaluate the hypothesis

Discovery-based science

• Uses observations as the primary way of collecting data.

• Does not necessarily involve an experiment or hypothesis.

• Can produce:

  • Qualitative data → descriptive/non-numerical observations

  • Quantitative data → numerical measurements

Hypothesis-based science

• Uses experiments to test a hypothesis.

• A hypothesis should be based on previous research.

• A basic format:

If the independent variable is changed, then the dependent variable will change.

Variables

Independent Variable (IV) What the researcher deliberately manipulates.

Dependent Variable (DV) What is measured in response to the independent variable. The dependent variable should be measurable and specific. Avoid subjective terms such as "better" or "sick."

Standardized Variables Everything else that could affect the experiment that needs to remain the same between groups.

Examples from the H₂S experiment:

• Diet

• Feeding times

• Time blood pressure is measured

• Duration of exposure

• Environmental conditions

• Number of mice per group

If these aren't kept constant, another variable could explain the results.

Hypothesis vs. Null Hypothesis

Hypothesis / Alternative Hypothesis Predicts that there will be a relationship or difference.

Example: If H₂S concentration increases above normal, then mouse blood pressure will decrease.

Null Hypothesis Predicts no difference between the groups.

Example: H₂S concentration will have no effect on mouse blood pressure.

Reject the null hypothesis → evidence suggests there is a difference.

Accept the null hypothesis → evidence does not indicate a difference.

Controls

Negative Control Expected to produce no change in the dependent variable. Usually does not receive the experimental treatment.

Positive Control Expected to produce a known/expected result. It helps demonstrate that the experimental method is working correctly.

The manual's DNA example uses a sample known to contain DNA as the positive control.

Positive control = should work

Negative control = should not change

Replication

Experiments need to be repeated.

Why?

• Makes results more reliable

• Helps determine whether results are consistent

• Reduces the chance that an unusual result is responsible for the conclusion

Scientific Notation

Basic rule

Move the decimal so that there is one non-zero digit to the left of the decimal.

Large number: 6,250,000,000 = 6.25 × 10⁹

Small number: 0.000028 = 2.8 × 10⁻⁵

Large number → positive exponent

Small number → negative exponent

The exponent tells you how many places the decimal must move to return to the original number.

Accuracy vs. Precision

Accuracy How close a measurement is to the actual/true value.

Precision How close repeated measurements are to each other.

You can be:

• Accurate and precise

• Accurate but not precise

• Precise but not accurate

• Neither

Significant Figures

Rules you NEED to know

-All non-zero numbers are significant.

-Leading zeros NOT significant.

Example:

0.003 → 1 sig fig

Zeros between non-zero numbers

Significant.

Example:

308 → 3 sig figs

Trailing zeros

Depends on the decimal.

300 → 1 sig fig

300. → 3 sig figs

300.0 → 4 sig figs

0.0030 → 2 sig figs

0.00308 → 3 sig figs

The manual specifically emphasizes that zeros are significant when they are not merely placeholders.

Rules for Calculations

Addition/Subtraction Answer has the same number of decimal places as the measurement with the fewest decimal places.

Example: 12.34+ 5.2 = 17.54

Final answer: 17.5

Multiplication/Division

Answer has the same number of significant figures as the measurement with the fewest significant figures.

Example: 4.5 × 2.22 × 7.1

The least number of sig figs is 2.

Therefore, the answer should have 2 significant figures.

Do NOT round in the middle of your calculations. Calculate everything first, then round the final answers.

Metric Conversions

Prefix	Symbol	Exponent
tera	T	10¹²
giga	G	10⁹
mega	M	10⁶
kilo	k	10³
hecto	h	10²
deka	da	10¹
base	—	10⁰
deci	d	10⁻¹
centi	c	10⁻²
milli	m	10⁻³
micro	μ	10⁻⁶
nano	n	10⁻⁹
pico	p	10⁻¹²

Example

2540 cm → km

cm = 10⁻²
km = 10³

Difference = -2 − 3 = -5

2540 cm = 0.02540 km

Measuring Equipment

Graduated cylinder Good for measuring liquid volumes.

Volumetric flask Most accurate, but designed for one specific volume.

Beaker / Erlenmeyer flask Do NOT use these to accurately measure liquid volume. Their markings can have significant error.

Meniscus When reading a liquid in a graduated cylinder: Read the bottom of the meniscus.

Measurement Precision

When using a measuring device:

Record all certain digits + ONE estimated digit.

Never record more than one estimated digit.

Pipettes

Know the basic parts/use:

• Pipette has a specific volume range.

• Never set it outside its range.

• Always use a tip.

• First stop → desired volume

• Second stop → blow-out

• Release plunger slowly.

• Never turn the pipette upside down.

• Liquid should stay in the tip.

LAB 2 — INTRODUCTION TO MICROSCOPY

LAB 2 — INTRODUCTION TO MICROSCOPY

Microscope Types

Your microscope is a: Binocular, compound, light microscope

Binocular = two oculars/eyepieces.

Compound = uses more than one lens:

• Ocular

• Objective

Light microscope = uses visible light.

Microscope Objectives

Objective	Magnification
Scanning	4×
Low power	10×
High power	40×
Oil immersion	100×

The ocular is normally 10×

Total Magnification

Formula: Total magnification = ocular × objective

Examples:

10× ocular × 4× objective = 40×

10× ocular × 40× objective = 400×

10× ocular × 100× objective = 1000×

The 1000× magnification is the upper limit of the resolving power of this light microscope.

Microscope Parts

Oculars= Eyepieces; standard magnification is 10×.

Objectives= Provide different levels of magnification.

Revolving nosepiece= Holds objectives and allows you to switch between them.

Stage= Where the slide sits.

Stage holder= Keeps the slide in place.

Mechanical stage adjusters= Move the slide.

Condenser= Focuses light onto the specimen.

Does NOT magnify.

Iris diaphragm= Controls the amount of light passing through the specimen.

Coarse focus= Moves stage significantly.

Only use with scanning objective.

Fine focus= Moves stage very slightly.

Can be used with all objectives.

Arm= Used to carry the microscope.

Base= Supports the microscope.

Oil Immersion

The 100× objective requires immersion oil.

Why? The oil reduces refraction of light, allowing the microscope to produce a better image at high magnification.

Once you use oil:

• Do NOT put the other objectives into the oil.

• Clean the oil immersion objective separately.

• Use only fine focus.

Image Orientation

The image reaching your eyes through the microscope is upside down because of the prism system.

Specimens

Possible specimens included:

• Cheek cells

• Bacillus megaterium

• Tardigrades

• Elodea

The manual emphasizes recording detailed observations as qualitative data.

Elodea and Salt Solutions

You observed Elodea in:

• 0 mM

• 150 mM

• 400 mM

You should understand that changing the surrounding solution changes water movement across the cell membrane.

Plant cells have a cell wall, which affects how the cell responds to changes in water movement.

Animal cells do not have a cell wall, so their response can be different.

LAB 3 — INTRODUCTION TO DESCRIPTIVE STATISTICS

LAB 3 — INTRODUCTION TO DESCRIPTIVE STATISTICS

Sample Size

n = number of measurements/samples

Larger sample sizes generally provide a better representation of the population.

Histogram

A histogram shows the distribution of a data set.

It can show:

• Range

• Frequency

• Shape of the distribution

Values are grouped into bins.

Central Tendency

Mean= Average.

Formula= Mean = Σx / n

Use the mean when the data are parametric/approximately normally distributed.

Median

The middle value after arranging values from smallest → largest.

If there is an even number of values:

Average the two middle values.

Median is particularly useful when data are skewed/non-parametric.

Mode

The value that occurs most frequently.

Useful when data have multiple peaks.

The statistics lecture also emphasizes mean for parametric data, median for skewed data, and mode for multiple peaks.

Normal Distribution

A normal/parametric distribution has a bell-shaped curve.

Non-parametric data may be:

• Skewed

• Have multiple peaks

Standard Deviation — SD

Standard deviation describes the variability of individual measurements around the mean.

High SD → Lots of variability.

Low SD → Measurements are closer together.

The lab manual explains that approximately 68% of individual measurements fall within ±1 SD of the mean for a normal distribution. Approximately 95% fall within ±2 SD.

Standard Error — SE

Formula= SE = SD / √n

SE tells you about the reliability of the mean.

Larger sample size → Smaller SE

Smaller SE → More reliable mean

This relationship is emphasized in both the statistics material and lab manual.

95% Confidence Interval

Formula= 95% CI = ± SE × 1.96

This gives the range around the mean.

Example:

Mean = 50
SE = 2

95% CI: 2 × 1.96 = 3.92

Therefore: 50 ± 3.92

Range: 46.08–53.92

The lab manual gives the same relationship: 95% CI = ± SE × 1.96.

Comparing 95% Confidence Intervals

NO overlap

→ Difference is likely statistically significant

→ Reject the null hypothesis.

Overlap

→ Results are inconclusive

→ Additional statistical testing would be needed.

The lab manual specifically states that non-overlapping confidence intervals suggest a statistically significant difference, while overlapping intervals are inconclusive.

E. coli Experiment

The lab investigated whether antibiotics affected E. coli growth.

Treatments included:

• No antibiotic

• Ampicillin

• Bactrim

Independent variable= Antibiotic treatment

Dependent variable= E. coli growth, measured using colony-forming units (CFUs).

Control= The no-antibiotic group is the negative control.

Serial Dilution

Serial dilution decreases concentration step-by-step.

Example: Each tube is diluted by a factor of 10. If you plate a diluted sample and count colonies, you use the dilution factor to estimate the original concentration.

Example from the manual

75 CFUs from a sample diluted 1,000×:

75 × 1,000 = 75,000 CFUs per 0.1 mL

Because 0.1 mL is 10× less than 1 mL:

75,000 × 10 = 750,000 CFUs/mL

Important range

The lab says plates containing approximately: 30–300 CFUs are typically used for the most accurate counts.

LAB 4 — EXCEL 1: DESCRIPTIVE STATISTICS

LAB 4 — EXCEL 1: DESCRIPTIVE STATISTICS

Excel Statistical Functions

Statistic	Excel function
Mean	AVERAGE
Median	MEDIAN
Mode	MODE.SNGL
Standard deviation	STDEV.S
Standard error	SD ÷ √n
95% CI	SE × 1.96

Standard Error in Excel

Example:

=STDEV.S(B3:B52)/SQRT(50)

Graph Type

For the descriptive statistics lab, you made a: Bar graph

Use a bar graph when comparing categories/groups.

Error Bars

When showing means, error bars should show the variability represented by the data.

Your lab specifically used 95% confidence intervals.

You need to know how to select: Custom → Specify Value

and select the cells containing the positive and negative error values

Graph Requirements

Know that your graph should have:

X-axis Clearly identify what is being compared.

Y-axis Clearly identify the measured variable and units.

Error bars Show the appropriate variability.

Legend Only needed when necessary to explain colors/shading/symbols.

Gridlines Optional; should improve readability.

Figure Caption

A figure caption should allow someone to understand the graph without needing the rest of the paper.

It should include:

• Figure number

• Title

• Important information about what is shown

• Relevant sample information

• What error bars represent

DO NOT put conclusions in the caption.

The manual specifically states that captions should not evaluate hypotheses or discuss conclusions.

Quantitative Comparative Statement — QC Statement

You need to know the 4 Cs.

1. Comparison

What two things are being compared?

2. Context

What characteristic is being compared?

3. Calculation

How are they mathematically related?

Includes:

• Magnitude → how much

• Direction → greater/lesser, increase/decrease

4. Clarity

Short, direct, and not repetitive.

The lab manual explicitly calls these the 4 Cs.

Example structure: Group A had a 25% greater mean height than Group B (from 160 cm to 200 cm).

You should be able to identify:

• Groups being compared

• Variable

• Percentage/difference

• Direction

LAB 5 — PROTEIN QUANTIFICATION WITH THE SPECTROPHOTOMETER

LAB 5 — PROTEIN QUANTIFICATION WITH THE SPECTROPHOTOMETER

Scientific Literature

Primary Literature

Most technical.

Scientists report their own experiments or fieldwork.

Usually contains:

• Title

• Abstract

• Introduction

• Methods and Materials

• Results

• Discussion

• Conclusion

• References

The paper used in your lab is primary literature.

Secondary Literature

Uses primary literature as its source.

Examples:

• Review articles

• Textbooks

• Handbooks

• Manuals

Tertiary Literature

Uses primary and secondary literature.

Examples:

• Science magazines

• Newsletters

• Newspaper science articles

• Introductory textbooks

Grey Literature

Scientific information not published in the usual journal/textbook formats.

Examples:

• Theses

• Dissertations

• Conference proceedings

Daphnia Article

The paper examined male and female Daphnia magna and their responses to environmental/thermal stress.

Important points:

Females

• Actively select habitats that provide good conditions for growth and reproduction.

• Experience more variable environmental conditions.

• Generally maintain higher HSP levels.

Males

• Tend to remain in deeper, colder water.

• This helps reduce exposure to some surface-water stresses.

• Generally have lower HSP levels.

The article reports that males occupied colder/deeper water than females and that females generally had higher levels of several HSPs.

Heat Shock Proteins — HSPs

HSPs help protect cells during stress.

HSP60

Helps with:

• Protein modification

• Repair of damaged proteins

• Preventing peptide accumulation

HSP70

• Molecular chaperone

• Helps maintain cellular homeostasis

• Helps proteins fold correctly

• Helps deal with protein aggregates

HSP90

• Helps proteins fold correctly

• Removes improperly structured proteins

• Helps with stress tolerance

• Helps regulate other proteins

These functions are described in the article used for the lab.

Spectrophotometer

A spectrophotometer uses light to measure how much light a substance absorbs.

Four basic components

1. Light

2. Filter

3. Sample holder

4. Receiver

Visible Light

Visible light is approximately: 380–740 nm

Wavelength is the distance between corresponding points on adjacent waves.

Blank

The blank is used to calibrate the spectrophotometer.

It accounts for background absorbance from things such as:

• Solution

• Cuvette

• Unbound dye

In your lab, the blank contained PBS.

Bradford Assay

The Bradford assay uses:

Coomassie Brilliant Blue (CBB)= CBB binds to proteins.

Unbound CBB → reddish-brown

Protein-bound CBB → blue

More protein:

→ more CBB binds

→ darker blue

→ greater absorbance

Wavelength

The Bradford assay is measured at: 595 nm

because bound CBB most effectively absorbs yellow light around this wavelength.

The lab materials state that absorbance at 595 nm is used to quantify protein.

Relationship to Remember

More protein

↓

More CBB bound

↓

Darker blue

↓

More absorbance at 595 nm

This relationship is one of the most important concepts from Lab 5.

Standard Curve

A standard curve uses samples with known protein concentrations.

You measure their absorbance.

Then:

1. Graph protein concentration vs. absorbance.

2. Create a best-fit line.

3. Obtain the equation.

4. Use the unknown sample's absorbance.

5. Solve for its protein concentration.

The lab manual explains that standard curves allow you to use known concentrations to determine the concentration of unknown samples.

Standard Curve Equation

y = mx + b

Where:

• y = absorbance

• x = protein concentration

• m = slope

• b = y-intercept

Example from your lab: 0.4 = 0.5176x + 0.0041

Solve for x: x ≈ 0.76 mg/mL

Pipettes in Lab 5

P1000

100–1000 μL

P200

20–200 μL

P20

2–20 μL

Always use the correct pipette for the volume you need.

Daphnia Experiment

The experimental groups were:

Group	Daphnia	Treatment
A	Male	Deionized water
B	Male	Deionized water + toxin
C	Female	Deionized water
D	Female	Deionized water + toxin

There were six males and six females for each treatment, with each replicate consisting of one Daphnia.

Independent variables

The experiment involves:

• Sex

• Toxin exposure

Dependent variable

HSP concentration

Standardized variables

Examples:

• Temperature

• Time exposed

• Volume of solution

• Container

• Environmental conditions

Models

A scientific model:

• Represents a hypothesis

• Helps explain a process

• Helps organize thinking

• Can reveal gaps in knowledge

• Can be revised when new data are collected

The protein lab specifically describes models as physical representations of hypotheses that can be revised as more data are collected.

LAB 6 — CELL TRANSPORT & OSMOSIS

LAB 6 — CELL TRANSPORT & OSMOSIS

Tonicity

Tonicity describes the concentration of solutes outside the cell relative to inside the cell.

Hypertonic

-More solute outside the cell.

-Water moves: OUT of the cell

Hypotonic

-Less solute outside the cell.

-Water moves: INTO the cell

Isotonic

-Same solute concentration inside and outside.

-There is no net movement of water.

The lab manual defines tonicity based on the solute concentration of the surrounding environment relative to the cell.

Diffusion

Movement of a substance from high concentration → low concentration.

Moves down the concentration gradient.

Does not require additional cellular energy.

Osmosis

Diffusion of water across a membrane.

Water moves toward the area with the higher solute concentration.

Membrane Transport

Small, nonpolar, uncharged molecules: Can pass directly through the phospholipid bilayer.

Example:

O₂

Large, polar, or charged molecules

Need transport proteins.

This is: Facilitated diffusion

Transport proteins help substances move across the membrane.

Biological membranes are therefore selectively permeable.

Aquaporins

Aquaporins are transport proteins that primarily transport: Water

Avogadro's Number

1 mole = 6.022 × 10²³ particles

Know this number.

Molar Mass

Molar mass = mass of one mole of a substance.

Units: g/mol

Example: Water = approximately 18 g/mol

Molarity

Formula= M = moles / liters

Molarity tells you the number of moles of solute per liter of solution.

Making a Solution

For a solution: grams needed = M × volume (L) × molar mass

Example:

0.5 M sucrose
342 g/mol
1 L

0.5 × 1 × 342 =

171 g sucrose

Stock Solutions

Know: C₁V₁ = C₂V₂

Where:

• C₁ = starting concentration

• V₁ = starting volume

• C₂ = desired concentration

• V₂ = desired final volume

The lab specifically used a 1 M sucrose stock solution to make different sucrose concentrations.

Sucrose Solutions

You worked with:

• 0 M

• 0.1 M

• 0.2 M

• 0.4 M

• 0.8 M

For a final volume of 100 mL from a 1 M stock:

Desired concentration	1 M stock needed
0.1 M	10 mL
0.2 M	20 mL
0.4 M	40 mL
0.8 M	80 mL

Then add water until the total volume is 100 mL.

Potato/Zucchini Osmosis Experiment

You measured:

• Initial mass

• Final mass

• Change in mass

• Percent change in mass

Change in mass= Final mass − Initial mass

Percent change in mass= (Final mass − Initial mass) / Initial mass × 100

The manual gives this exact calculation for the potato and zucchini experiment.

Interpreting Percent Change

Positive percent change

The tissue gained mass.

→ Water entered the tissue.

Negative percent change

The tissue lost mass.

→ Water left the tissue.

Approximately 0% change

Little/no net movement of water.

This corresponds to approximately isotonic conditions.

Osmolarity

The goal of the experiment was to determine the osmolarity of potato and zucchini tissue.

On a graph of:

Sucrose concentration vs. % change in mass

look for where:

% change in mass = 0

That is the approximate osmolarity/isotonic point of the tissue.

The manual specifically asks where on the graph you would determine osmolarity and what type of solution would reveal it.

CALCULATIONS YOU SHOULD PRACTICE

Lab 1

• Scientific notation

• Significant figures

• Addition/subtraction rounding

• Multiplication/division rounding

• Metric conversions

• Mean

Lab 3

• Mean

• Median

• Mode

• Standard deviation

• Standard error

• 95% CI

• Serial dilution/CFU calculations

• Determine significance from confidence intervals

Lab 5

• Standard curve

• y = mx + b

• Solve for unknown protein concentration

Lab 6

• Molar mass

• Molarity

• C₁V₁ = C₂V₂

• Grams needed for a solution

• Change in mass

• Percent change in mass

• Identify isotonic point/osmolarity

EXCEL/GRAPHING SKILLS TO KNOW

Descriptive statistics graph

Bar graph

Usually:

X-axis = groups/categories

Y-axis = measured variable + units

Bars = means

Error bars = 95% CI

You should know:

• How to calculate mean in Excel

• How to calculate SD in Excel

• How to calculate SE

• How to calculate 95% CI

• How to add custom error bars

• How to label axes

• How to include units

• What a figure caption should contain

• What a legend is used for

• How to interpret overlapping/non-overlapping error bars

• How to write a QC statement

The Excel lab specifically focuses on descriptive statistics and creating bar graphs with error bars.

MOST IMPORTANT THINGS TO MEMORIZE

1. Independent vs. dependent variable

2. Standardized variables

3. Positive vs. negative control

4. Null vs. alternative hypothesis

5. Replication

6. Accuracy vs. precision

7. Significant figure rules

8. Metric prefixes

9. Microscope objective magnifications

10. Total magnification = ocular × objective

11. Coarse vs. fine focus

12. Why oil is used with 100×

13. Mean vs. median vs. mode

14. SD vs. SE

15. SE = SD/√n

16. 95% CI = SE × 1.96

17. What overlapping CI means

18. Serial dilution

19. 30–300 CFUs

20. Excel statistical functions

21. Bar graph + 95% CI error bars

22. QC Statement 4 Cs

23. Bradford assay

24. 595 nm

25. More protein → more blue → more absorbance

26. Standard curve

27. y = mx + b

28. Hypertonic/hypotonic/isotonic

29. Diffusion vs. osmosis

30. Aquaporins

31. 6.022 × 10²³

32. Molarity

33. C₁V₁ = C₂V₂

34. Percent change in mass

35. Osmolarity = point where % mass change is approximately 0

One especially important distinction

Don't mix these up:

SD → variability of INDIVIDUAL measurements

SE → reliability of the MEAN

95% CI → range used when comparing means