General Biology Lab Skills: Microscopy, Scientific Inquiry, Spectrophotometry, and Graphing

Microscopy (Lab 1)

Introduction

Microscopy is a fundamental technique in biology that allows for the observation of structures too small to be seen with the naked eye. Understanding the parts and functions of a microscope, as well as how to calculate magnification and cell size, is essential for laboratory work.

• Parts of a Microscope: Recognize and identify the main components, such as the ocular lens (eyepiece), objective lenses, stage, coarse and fine adjustment knobs, light source, and diaphragm.

• Order of Adjustments: When viewing a slide, start with the lowest power objective lens and use the coarse adjustment knob to focus. Switch to higher power objectives as needed, using the fine adjustment knob for precise focusing.

• Total Magnification: Calculate by multiplying the magnification of the ocular lens by the magnification of the objective lens. Formula: $\text{Total Magnification} = \text{Ocular Magnification} \times \text{Objective Magnification}$

• Unit Conversions: Be able to convert between meters (m), centimeters (cm), millimeters (mm), and micrometers (μm). Examples: 1 mm = 1,000 μm 1 cm = 10 mm

• Calculating Cell Size: Use the measured field of view and total magnification to estimate the size of a cell.

Example:

If the field of view is 2 mm at 100x magnification, and a cell spans half the field, its size is approximately 1 mm.

Scientific Inquiry (Lab 2)

Introduction

Scientific inquiry involves forming hypotheses, designing experiments, and analyzing data. The chi-square test is a statistical method used to compare observed and expected data.

• Null Hypothesis: A statement that there is no effect or difference. It must be testable and falsifiable.

• Alternative Hypothesis: States that there is a difference or effect between groups.

• p-value: The probability of obtaining results at least as extreme as those observed, assuming the null hypothesis is true. A typical threshold for significance is 0.05.

• Chi-Square Test: Used to compare observed data to expected data. Formula: $\chi^2 = \sum \frac{(O_i - E_i)^2}{E_i}$ where $O_i$ = observed value, $E_i$ = expected value.

• Degrees of Freedom (df): Calculated as the number of categories minus one ($df = n - 1$).

• Critical Value: The value from the chi-square table that the calculated chi-square must exceed to reject the null hypothesis at a given significance level.

Example:

If your calculated $\chi^2$ is greater than the critical value at $p = 0.05$ for the appropriate degrees of freedom, you reject the null hypothesis.

Critical Values of $\chi^2$ Table

df	p = 0.05	p = 0.01
1	3.84	6.64
2	5.99	9.21
3	7.82	11.34
4	9.49	13.28
5	11.07	15.09

Spectrophotometry (Lab 3)

Introduction

Spectrophotometry is a technique used to measure how much light a solution absorbs. This information can be used to determine the concentration of solutes and study enzyme activity.

• Light Intensity and Solute Concentration: The amount of light absorbed by a solution is proportional to the concentration of the absorbing substance (Beer-Lambert Law).

• Sources of Error: Errors can arise from instrument calibration, sample contamination, or improper cuvette handling. These can be corrected by proper calibration and technique.

• Temperature and pH Effects: Both factors can affect enzyme activity, altering the rate of reactions measured by spectrophotometry.

• Transmittance and Pigmentation: The color of a solution affects how much light passes through it, which can be measured as transmittance or absorbance.

Example:

Measuring the absorbance of a solution at 600 nm can indicate the concentration of a pigment or enzyme product.

Graphing (Lab 4)

Introduction

Graphing is essential for visualizing experimental data and identifying trends or relationships between variables.

• Graph Types: Choose the appropriate graph (bar, line, scatter plot) based on the type and number of variables.

• Variables: Identify dependent (measured) and independent (manipulated) variables.

• Axes: Plot independent variables on the x-axis and dependent variables on the y-axis.

• Figure Legends: Provide clear explanations and suggest additional data that could enhance the graph's interpretation.

Example:

A scatter plot of enzyme activity (y-axis) versus temperature (x-axis) can reveal the optimal temperature for enzyme function.