AP BIOLOGY REVIEW: LABS

Lee Ferguson

Allen High School

Allen, Texas

SOME HOUSEKEEPING ITEMS

• Not all questions that were asked prior to the session will be addressed in this live broadcast.
• They will, however, be addressed in a doc I’ll link below.
• You can also have a copy of this presentation so be sure to check the list of links below for it.
• This presentation is addressing the labs that had the most questions asked about them.

HOW TO USE THIS REVIEW

• To brush up on data analysis skills
• To review data presentation skills
• To review experimental design skills
• To jog your memory about the lab work you’ve done during the year
• This is not a substitute for actually DOING the labs!

PRESENTER’S NOTE

• Be advised that not all teachers do the labs exactly as they are published in the College Board AP Biology Lab Manual
• The labs discussed in this broadcast are representative of labs done by many teachers across the country and may or may not mirror exactly what your teachers did with you

DATA ANALYSIS SKILLS

STATISTICAL ANALYSIS

• You’ll need to be able to interpret statistics such as:
• Mean
• Standard Deviation*
• Standard Error of the Mean
• 95% Confidence Interval

MEAN

• The average of a set of data points.
• We use this as the starting point for things like standard deviation, standard error of the mean (SEM) and the 95% confidence interval
• You may be asked to calculate this for a given data set.

STANDARD DEVIATION

• Based on the normal distribution (“bell shaped curve”)
• Shows you how far away from the mean your data points are
• Note: You do NOT need to know how to calculate SD, only to interpret its significance

Mean of sample

STANDARD ERROR OF THE MEAN: �WHAT DOES IT MEAN?

• The standard error of the mean (SEM) is used to determine the error that exists in taking data from only a sample of a population—we can’t ever really sample an ENTIRE population
• Because of this, there is error inherent in the data we collect
• Standard error is a measure of precision: how good are our data? How reliable are our data?

STANDARD ERROR OF THE MEAN AND ERROR BARS: HOW DO YOU CALCULATE IT?

• The standard error of the mean (SEM) is calculated by using the standard deviation of the data set divided by the square root of the number of samples in the data set
• Note: don’t memorize the formula—that’s what your formula chart is for
• You will be given the SD should you have to calculate standard error

ERROR BARS ON GRAPHS: WHAT AND HOW

• Error bars on graphs are placed above and below the center of the bar
• They can represent either the standard error above and below the mean
• They can also represent the 95% confidence interval above and below the mean
• You may see BOTH kinds of graphs on the exam
• You may be asked to produce a graph like this (see 2014 FRQ #1)

HOW TO READ ERROR BARS

When error bars do NOT overlap:

There IS a statistically significant difference between the means of the two groups and more testing is needed

When error bars DO overlap:

There is NOT a statistically significant difference between the means of the two groups—more testing is needed

DATA PRESENTATION SKILLS: MAKING GRAPHS

• On an FRQ, you may be asked to produce a graph from a data set given as a part of the question
• Correctly constructed graphs meet several criteria
• Axes are labeled WITH UNITS
• Axes are oriented correctly (dependent variable on y-axis, independent variable on x-axis)
• Points are plotted correctly
• Graph is the correct type for data presented

EXPERIMENTAL DESIGN SKILLS

EXPERIMENTAL DESIGN SKILLS

• Occasionally you may be asked to:
• Design an experiment
• Evaluate an experimental design
• Propose a method for testing something
• Pose a question that can be tested
• State a hypothesis that can be tested
• These all require experimental design skills

DESIGNING AN EXPERIMENT

• Key components of good design:
• A testable question
• Variables:
• Dependent: the variable that is measured
• Independent: the variable that is changed to effect an outcome. There is only one of these changed at a time.
• Conditions held constant: this reduces confounding factors that prevent you from collecting high quality data. Also helps you to ensure that the independent variable is the only thing affecting your outcome.
• Control Group: the standard for comparison and the group which does not receive the independent variable.
• Repetition: multiple trials must be conducted to collect an ample amount of data for analysis

EVALUATING AN EXPERIMENT

• Look for the elements of good experimental design:
• Is there only one independent variable being tested at a time?
• Is there a control group for comparison?
• Did the experimenter hold all possible conditions constant except for the independent variable?
• Is the experimenter conducting repeated trials?

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AP BIOLOGY LABS

BIG IDEA 1: EVOLUTION

• Labs you may have done:
• Artificial Selection
• Population Genetics (Hardy-Weinberg Modeling)
• BLAST Lab

LAB: ARTIFICIAL SELECTION

• Question investigated: Can extreme selection change expression of a quantitative trait in a population in one generation?
• Key concepts:
• Natural selection acts on phenotypic variation in populations
• Natural selection is a major mechanism of evolution
• Set-up:
• Grow a set of FastPlants for several generations and select for the number of trichomes (hairs on the leaves)
• Find plants with highest number of trichomes and selectively breed them

LAB: ARTIFICIAL SELECTION

• Expected results:
• You should expect to see directional selection for the number of trichome hairs as the generations increase in number
• See FRQ #1, 2014 exam for sample question about this

LAB: HARDY WEINBERG MODELING

• Question investigated: How can mathematical models be used to investigate the relationship between allele frequencies in populations of organisms and evolutionary change?
• Key concepts:
• Natural selection acts on phenotypic variation in populations (which ultimately leads to changes in genotype)
• Natural selection is a major mechanism of evolution
• Evolutionary change is driven by random processes
• Populations of organisms continue to evolve

LAB: HARDY WEINBERG MODELING

• Set-up:
• Mathematical modeling of population genetics using a spreadsheet to view changes in allele frequencies over time
• Given certain scenarios (selection, genetic drift, etc.), spreadsheet had to be altered to reflect change in allele frequencies
• Be familiar with the equation:

p² + 2pq + q² = 1

Where p + q = 1

because probability of inheriting a dominant

or recessive allele is ½

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LAB: HARDY WEINBERG MODELING

• Expected result:
• You should expect to see changes in allele frequency based on whether or not the conditions of a population in Hardy-Weinberg equilibrium are present
• Population is large
• No selection occurs
• Mating is completely random
• No mutations occur
• No gene flow
• If all of these occur simultaneously, allele frequencies will not change, and evolution of the population will not occur

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LAB: BLAST

• Question investigated: How can bioinformatics be used as a tool to determine evolutionary relationships?
• Key concepts:
• Natural selection acts on phenotypic variation in populations
• Biological evolution is supported by scientific evidence from many disciplines including mathematics
• Phylogenetic trees and cladograms are models of evolutionary history that can be tested
• DNA, and in some cases, RNA is the primary source of heritable information

LAB: BLAST

• Set-up:
• Use a set of DNA sequences and the BLAST database to determine evolutionary relationships among a group of organisms
• Use the data gathered about the similarities between genes and protein sequences of a set of organisms to create a cladogram to illustrate evolutionary relationships
• Use information found in BLAST to compare gene sequences from an “unknown” fossil to genes of existing organisms to allow placement of fossil organism on cladogram with currently living organisms

LAB: BLAST

• Creating Cladograms from Data:
• Examine the data set given:

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• The cladogram at right is generated from the data set shown above.

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The nodes (green circles) you see above indicate branching points where speciation occurred, but they also indicate a common ancestor.

BIG IDEA 2: ENERGY AND COMMUNICATION

• Labs you may have done:
• Diffusion and Osmosis
• Photosynthesis
• Respiration

LAB: DIFFUSION AND OSMOSIS

• Question investigated: What causes plants to wilt if they are not watered?
• Key concepts:
• Growth, reproduction and dynamic homeostasis require that cells create and maintain internal environments that are different from their external environments
• Cell membranes are selectively permeable due to their structure
• Diffusion and osmosis are forms of passive transport that move molecules across membranes
• Water potential is the potential for water to do work (dissolving solutes): free energy of water

LAB: DIFFUSION AND OSMOSIS

• Set-up:
• Create artificial cell models to explore the relationship between surface area, volume and rate of diffusion
• Exploration of water potential by cutting plant tissue cores and soaking in sucrose solutions of varying molarity to determine sucrose concentration of plant tissue

LAB: DIFFUSION AND OSMOSIS

• Expected results:
• As cell gets larger, SA-V ratio decreases
• Water potential: water moves from areas of high water potential (hypotonic solutions) to areas of low water potential (hypertonic solutions)
• Plant tissue will gain mass in hypotonic solutions
• Plant tissue will lose mass in hypertonic solutions

LAB: DIFFUSION AND OSMOSIS

• Calculating water potential of plant tissue from experimental results:
• Remember that the formula for water potential is

𝚿= -iCRT

Where i = ionization constant (1 for non-ionic solutes)

C = molar concentration of solution (mol/L)

R = 0.0831 L-bars/mol-K

T = temperature in degrees K

LAB: DIFFUSION AND OSMOSIS

• More on water potential:
• A high water potential means that there is little solute in the solution and that its value is closer to zero. Remember, a water potential value of zero means there is no solute in the solution (if pressure potential is equal to zero).
• A low water potential means that there is solute in the solution. Its value will be negative, but how negative it will be is determined by the following factors:
• Molar concentration of solute
• Ionization constant
• High temperature
• All of these will cause the value for water potential to become more negative

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BIG IDEA 3: INHERITANCE

• Labs you may have done:
• Mitosis/Meiosis
• Transformation
• Restriction Enzyme Analysis of DNA (electrophoresis)

LAB: RESTRICTION ENZYME DIGEST

• Question investigated: How can we use genetic information to identify and profile individuals?
• Key concepts:
• Populations continue to evolve
• DNA is the primary source of hereditary information
• The chromosomal basis of inheritance provides an understanding of how genes are passed from parent to offspring
• Restriction enzymes recognize certain sequences of DNA and will cut the DNA at those sites, creating fragments of varying sizes

LAB: RESTRICTION ENZYME DIGEST

• Set-up:

LAB: RESTRICTION ENZYME DIGEST

• Expected Results:
• The gel at right shows DNA samples from individuals who:
• Have normal hemoglobin (DNA is normal and thus cut since the restriction site is recognized by the restriction enzyme)
• Have sickle trait (heterozygotes, so have cut DNA and uncut mutant DNA)
• Have SCA (mutant homozygotes, thus uncut DNA because the restriction site cannot be recognized by the enzyme)

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Sickled Hgb (SS)

Sickle Trait (AS)

Normal Hgb (AA)

BIG IDEA 4: INTERACTIONS

• Labs you may have done:
• Energy Dynamics
• Transpiration
• Animal Behavior
• Enzyme Activity

LAB: ANIMAL BEHAVIOR

• Question investigated: What environmental factors trigger a fruit fly/pillbug/jewel wasp/mealworm response?
• Key concepts:
• Experimental Design principles
• Interactions within populations influence patterns of species distribution
• Timing and coordination of behavior are regulated by various mechanisms and are important in natural selection

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LAB: ANIMAL BEHAVIOR

• Set-up: Choice chambers with different environments are set up and organisms are allowed to explore the environment. Data about how many organisms are on one side of the choice chamber or the other are recorded at regular intervals.

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LAB: ANIMAL BEHAVIOR

• Analysis: A chi square test is appropriate here. Let’s see how to do one for this situation.
• Example: An experiment to determine the effect of a glucose solution on pillbug behavior was conducted. Students set up a choice chamber with two sections. One section had a cotton ball wet with distilled water, and the other section had a cotton ball wet with a glucose solution.
• Null hypothesis: The pillbugs will prefer the water and glucose equally. (in other words, there is no difference between what is expected and what is observed)
• Alternative hypothesis: The pillbugs will demonstrate a preference for one solution over the other.
• Data were collected over a period of 20 minutes.

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LAB: ANIMAL BEHAVIOR

• Analysis: Let’s look at the data the students collected

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Question: Calculate the chi-square for the 20 minute time point. Will the student reject or fail to reject their null hypothesis based on the data?

Remember the chi square formula looks like this:

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(it’s on your formula chart, so don’t memorize it)

LAB: ANIMAL BEHAVIOR

• Now, set up your chi-square:

Observed values: these are the values obtained at the 20 minute time point.

Expected values: these are based on the null hypothesis. Since the null hypothesis states that the pillbugs will be equally distributed, your chi-square table will look like this:

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Now to interpret this value...you need:

Degrees of freedom: this is the number of choices available to the pillbugs, minus 1. In this investigation it was 2 choices, so the degrees of freedom here is 1.

p-value: this is always 0.05, and represents the fact that we are 95% sure that any deviation from expected values is due to chance alone.

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LAB: ANIMAL BEHAVIOR

Use the critical values table (it’s on your formula chart too):

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For one degree of freedom at a p value of 0.05, the critical value of chi-square is 3.84.

Our calculated value for chi-square from our table on the previous slide was 3.2

So what does this mean?

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Since our calculated value is SMALLER than the critical value, we must FAIL TO REJECT (some teachers say “accept”) the null hypothesis. In other words, any deviation from expected values is strictly due to chance and not some other factor. Another way to look at this is that the independent variable had no effect on the dependent variable.

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If our calculated value was LARGER than the critical value, we would REJECT our null hypothesis. There IS an outside factor influencing our outcome, and our independent variable DOES have an effect on our dependent variable. Our results are NOT due to chance.

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FINAL TIPS

• Always look for patterns in data
• What are the general trends?
• Look at the big picture
• For models, look at what is being represented
• What biological process is this representing or attempting to represent?
• What are the limitations of the model?

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REMEMBER…

• You’ve worked really hard all year long—give it your best shot and show us the biology that you know!

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More resources

Lab Summary Slide Show: https://drive.google.com/open?id=0B81el0loEfBEWnNQbWE1TnkzM00

Bozeman Biology Lab Summaries: https://www.youtube.com/watch?v=CMIdyMDHd78

https://youtu.be/YH6Cpo1OqJs