Transcript Biology Basics Powerpoint Notes

Foundations of Biology!
What’s the BIG IDEA?
EVOLUTION
INTERACTION
ENERGY
INFORMATION
ORGANIZATION
What’s the BIG IDEA?
EVOLUTION – the core theme!
Populations of organisms change over time
INTERACTION
Organisms must interact with each other and the environment
ENERGY
Life requires transfer and transformation of energy & matter
INFORMATION
Organisms must express and pass on genetic information
ORGANIZATION
Organisms maintain order & link structure & function
Organization – life is ordered!
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From atoms to ecosystems, the structure
determines the function
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Molecules: enzymes must perfectly match their
substrates; order of bases in DNA sets the code
Cells: nerve cells have many branches to better
send and receive signals
Organs: the stomach has thick muscle to allow
churning
Ecosystems: rainforest canopy vs mid-level vs
floor (different “ways of life” at each level)
Organization – life is ordered!
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Cells are basic units of life…all life functions
happen at the cellular level
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Have specific structures to do specific jobs
(organelles)
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Ex: mitochondria – curvy surface to make more ATP
Prokaryotic = simple, non-nucleus cell; bacteria
Eukaryotic = fancier cell with nucleus & the
fancy organelles; all other cells
Organization – life is ordered!
…apparent at ALL
levels!
Does this cost energy
or give off energy to
maintain?
Organization – life is ordered!
Form fits function
The way something is put together
determines how it works
What is an example of this on a human?
Organization – the hierarchy…
Atom
Molecule
Organelle
Cell
Tissue
Organ
Organ System
Organism
Population
Community
Ecosystem
Organization –
Life maintains homeostasis
Maintain internal environment
What sorts of things must be
kept stable within you?
Once the value is set, why
doesn’t it just stay there?
Information
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DNA holds code for all types of life functions
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All organisms use this, and it is same structure
Proteins made with same process in all life
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DNA copied into RNA which builds proteins
ALL organisms share these
…why is that significant?
Information
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Genetic code passed to offspring
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By combining sexually – meiosis
By exact copying – mitosis & asexual reproduction
Why is it so important to pass on these codes?
Do all organisms do this?
Information Life reproduces itself
life comes from life
…applies to cells as well as whole organisms
So where did the first life come from?
Recap…
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Predict 4 characteristics that the first cells
would have had.
Defend your choices.
Information
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Organisms send and receive signals
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Internal – hormones, nerve impulses, …
Exterior – sounds, sights, etc
Why is it so important to pass on these signals?
Do all organisms do this?
Information - Signaling
Must take in info and
respond accordingly
What is an example of
your body doing this?
Does this happen at the
cell level?
Energy – at all levels!
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All organisms make ATP by breaking down
glucose… which is made by the green things
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Ecosystem: sun  autotrophs  heterotrophs
Chloroplasts: light + CO2  glucose
Mitochondria: glucose  ATP + CO2
Why is it so important to transform energy?
Do all organisms do this?
Energy – at all levels!
need energy to do work …at ALL levels!
What kind of work does “life” do?
Interaction
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Life depends on the environment
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Abiotic factors: light, water, O2, etc
Ex: plants use light & CO2 & water to make sugar
Life depends on other living things
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Biotic factors: symbiosis, parasitism, predation, etc
Ex: flowers needs bees for pollination
What is the payoff for this interaction?
What is the risk?
Evolution – the core theme!!!
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Descent with modification
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Populations of organisms share common
ancestors, but make changes to genes over long
periods of time
Natural selection
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Survival of the fittest: genes that give better traits
move on to next generation (reproductive success)
Depends on variation (what nature selects from)
Why can’t you personally evolve?
Evolution – unity in diversity
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ALL organisms share certain traits (like
glycolysis, DNA, ATP, cell structures,…)
Many species exist…all with varied traits
3 Domains…6 Kingdoms …what are they called?
Evolution – unity in diversity
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3 Domains
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Archaea – extreme-habitat prokaryotes
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Bacteria – “common bacteria”; prokaryotes
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Eukarya – all eukaryotes (animals, plants, fungi,
protists)
The End
Science as a
Process
Chapter 1 – Part 2
Scientific
Method
Scientific
Method
It starts with a question, then…
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You make a hypothesis
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Tentative explanation based on previous knowledge
Must be testable
Can be eliminated, but not confirmed with certainty
Does the amount of protein
affect growth rate of mice?
 What
would a proper hypothesis
for this question be?
Null Hypothesis
Assumption that the observed difference between two
samples is purely accidental
- not due to the effect the independent variable
has on the dependent variable
Example…
Ho = Adding more water to daisies will have no effect
on how tall they grow.
OR… for some types of experiments, the null will be
“the observed data & expected data are the same”
Null Hypothesis
If experiment is does not have an independent variable
the null will more likely be written as…
“there will be no difference between the expected data
and the observed data”
Example: when a red-eyed female fruit fly is crossed
with a white-eyed male fruit fly, what are the results?
(white is recessive, so we expect all babies to be red)
Null: All offspring will have red eyes.
Does the amount of protein
affect growth rate of mice?
 What
would a proper NULL
hypothesis for this question be?
Experimental Design
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Large sample size
Replicated many times
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Control Group
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The “baseline”…what results compared against
Not present in “comparative” investigations
Controlled Variables
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Remain the same between all groups, so that
they are NOT factors in the experiment
Experimental Design Variables
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Independent Variable
(“I” set up beforehand)
~ is the only variable that
is changed between
experimental groups
~ example: color of light
on plants
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Dependent Variable
(“Data” collected
“During” experiment)
~ is the effect of the
independent variable
~ it is what you measure
as you experiment
~ ex: height plants grow
Does the amount of protein
affect growth rate of mice?
 What
would the control for this
experiment be?
 What are the…
IV
 DV
 CV
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Experimental Design
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Bob wants to see which fertilizer will make his
tomato plants produce the largest # of tomatoes.
He uses the same variety of tomato, same amt & type of
soil, same amt of water & plants them in same area.
Group 1 gets no fertilizer, group 2 gets Brand Q, group 3
gets Brand R, group 4 gets Brand S.
Identify:
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IV
DV
CV
control group
experimental groups
Experimental Design
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Sue wants to see if plant food makes rose bushes
produce more flowers.
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Identify:
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IV
DV
CVs
Control group
Null hypothesis
On her data table, what would the labels be at the
top? (which one goes where?)
What would the labels on each axis of a graph be?
Data Tables
Independent Variable
(unit)
Dependent Variable
(unit)
Data Tables
Table 1. Height of Sunflowers when Grown in Varying Colors of Light
Color of Light
White (daylight)
Red
Blue
Green
Sunflower Height (cm)
7
6
8
3
Lab – Oreo Claim
I. Purpose: Do double-stuff Oreos really have
double the stuff?
II. Background
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What do you already know?
III. Hypothesis
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HO =
HA =
IV. Procedure
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IV=? DV=? CV=?
Normally Simple labeled sketch of procedure
For this one  easier to describe in words?
Do Double-Stuff Oreos really have
the double the stuff?
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Regular (x2)
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DS
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Analyzing Data
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Standard Deviation
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Standard Error of the Mean
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How spread out is the data?
How likely is it that our “mean” is a good value?
Chi-square test & t-Test
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How close to our “expected” is close enough?
Analyzing Data
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Standard Deviation:
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How spread out is the data?
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how far on average any data point is from the mean
the smaller the SD, the closer the scores are to mean
when SD is large, the scores are more widely spread
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Analyzing Data
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Standard Deviation:
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Moving a SD away from mean in either direction lets us
estimate what % of points are within that section
In science we almost always go with the 95%
confidence level… this is equal to +/-2 SD units
Analyzing Data
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Standard Deviation:
To calculate, you need to know…
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Individual data points (x)
Mean of data points ( )
Sample size (n)
Analyzing Data
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Standard Deviation:
Steps…
1. Calculate mean
2. Fill out chart with calculations
(subtract & square)
3. Add up last column
4. Divide that sum by (n-1)
5. Take square root of that number
Analyzing Data
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Standard Error of the Mean
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How likely is it that our “mean” is a good value?
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the higher the sample size, the more sure we can be
to calculate this, simple divide the standard deviation
value (s) by the square-root of the sample size.
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Yes, you have already done most of the work by now!
In science we almost always use 2xSEM to get to the
95% confidence level
Graph – DRY MIX
Graph 1. Height of Sunflowers when Grown in
Different Colors of Light
9
8
7
Height of
Sunflowers
(cm)
6
5
4
3
2
1
0
white
red
blue
Color of Light
green
Which Graph to Use?
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Check the x-axis (the IV)
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If it is numbers…make it a LINE graph
If it is words…make it a BAR graph
Which Graph to Use?
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Bar Graph
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For discrete
data…words on the X!
Which Graph to Use?
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Modified Bar Graph
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Includes error bars (see SEM)
Which Graph to Use?
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Line Graph/Scatter-Plot
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Line of best fit (regression line)
For continuous data…#s on the X!
Analyzing Data
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Standard Deviation
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Standard Error of the Mean
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How spread out is the data?
How likely is it that our “mean” is a good value?
Chi-square test & t-Test
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How close to our “expected” is close enough?
Analyzing Data
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Standard Deviation:
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How spread out is the data?
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how far on average any data point is from the mean
the smaller the SD, the closer the scores are to mean
when SD is large, the scores are more widely spread
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Analyzing Data
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Standard Error of the Mean
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How likely is it that our “mean” is a good value?
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the higher the sample size, the more sure we can be
to calculate this, simple divide the standard deviation
value (s) by the square-root of the sample size.
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Yes, you have already done most of the work by now!
Analyzing Data
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t-Test
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How close to our “expected” is close enough?
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Used to compare two data sets
Null hypothesis will say the data sets are the same…use
this to reject/accept that!
Plug in #s you already know…get t-value
use table to see if there is too much difference
between the sets to say that they are the same
Analyzing Data
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Chi-square test
How close to our “expected” is close enough?
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Will be taught later!
The End
Fake Quiz!
Answer on scratch paper (or in your notes).
Don’t look at your notes!
Answer everything!
Bob wants to see if having earthworms in the soil
will make his pepper plants produce more peppers.
He sets up 4 planting pots with soil, pepper plant
seedlings, & water. To the 4 pots he adds 0, 2, 4,
and 6 earthworms. He allows the plants to grow for 2
months, counting the total # of peppers produced during
that time.
1. What is the IV?
2. What is the DV?
3. List any 3 controlled variables that will be needed.
4. Which is the control group?
5. What is the null hypothesis for this experiment?
6. Which “characteristics of life” are involved in this
scenario? (Be able to defend your choices!)
Test Review!
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1. Calculating slope of line
2. Test format - discuss
3. Practice – free response ques: experiment
4. Practice – FRQ: graphing/stats
5. Check your list!
Calculate the Slope:
What is the rate between 2 and 6 minutes?
Calculate the Slope:
What is the rate between 1 and 5 seconds?
Test Format
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Mostly multiple-choice (about 60 pts)
Some numerical response (about 10 pts)
Free Response question (about 30 pts)
Numerical Response
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Are griddable on real AP Exam…write-in on ours
Units not required if it is the griddable kind
Do not have to show work unless specifically told
to do so!
Will be these: slope…mean…SD…SEx
Free Response – an example…
A student wondered whether radish seeds
require a certain number of hours of light in
order to sprout successfully.
 Identify the IV and DV. Describe measures taken
to allow for controlled variables.
 Write a null hypothesis for this experiment.
Write an alternate hypothesis for this experiment.
(Label each.)
Free Response – an example…
A student wondered whether radish seeds
require a certain number of hours of light in
order to sprout successfully.
 Design a controlled experiment to test the null
hypothesis. Describe data that would cause you
to reject your null hypothesis. Defend your
answer.
Lab – Mealworm Growth
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I. Purpose: Does temperature affect how
fast mealworms grow?
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Limitations/requirements:
1 week of growth
Will measure individual MWs before & after
Temps: Cold (~10)…Medium (~22)…Hot (~28)
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May want to pick only TWO, but can do more
Will be doing SD, SEM, t-Test
Lab – Mealworm Growth
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I. Purpose: Does temperature affect how
fast mealworms grow?
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Follow the lab format…green paper!
What value will you actually measure? (DV)
What variables do you need to control (CVs)
Is there a control group? If so, what?
What will data table(s) look like?
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Notes about the Lab Write-up…
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Please go get your lab notebook and
look through the mealworm lab
Notes about the Lab Write-up…
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MAJOR ISSUES:
The final data table should be a summary showing
the IV and DV (Temp vs Growth)
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All stats tests and graph are related to the
AVERAGE growth, not individual readings
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The graph needs to be stretched high enough that
data can actually be compared well
Notes about the Lab Write-up…
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MAJOR ISSUES:
The conclusion must state stats proof as to why you
are accepting/rejecting null
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Based on t-test or error bars… not “common sense”
“Since the t-value of 1.321 was lower than the critical cutoff value of 2.776, the null hypothesis must be accepted”
Then can discuss why this may be skewed…hot = pupa
The sample size was ridiculously tiny for all grps
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Error bars are actually supposed to be 2xSEM!!!
Would there be any way for your values to not be equal?
Notes about the Lab Write-up…
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MINOR ISSUES:
The “ I. Purpose” is a question
Think ahead as to what data you’ll be recording
…can make much more concise data recordings
(doesn’t have to be a table, but they are efficient!)
Due to tiny sample size, should’ve borrowed data
Include units in all measures (DTs and graphs)
Must include data summary (sentences)
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“the mean was higher in the hot than cold”…