Transcript The Scientific Method

The Scientific Method
Laboratory Skills and
Statistics
Topic 1
Laboratory Skills and Statistics
Topic 1 concerns Biological Statistics.
To be successful in IB, it is essential
that you have knowledge of statistics
to apply and analyze data you collect
during laboratory activities. To be
successful on your Internal
Assessments, I have put together this
PowerPoint and some other materials
that will be a useful resource for you.
The Scientific Method
Making Observations
Asking Questions
Forming a Hypothesis
Generating a Null Hypothesis
Making Predictions
Designing an Investigation
Testing the Predictions
Conclusion
Hypotheses and Predictions
HypothesisA scientific hypothesis is a possible explanation
for an observation or a scientific problem that is
given to you. Features of a sound hypothesis:
It offers an explanation for an observation
It refers to only one independent variable
It is written as a statement and not a question
It is testable by experimentation
It is based on further research, observations or
prior knowledge
It leads to predictions about the system (or the
topic of your experiment)
Hypotheses…let’s give it a try!
Example 1: During an experiment on
bacterial growth, the girls noticed that
bacteria in cultures grew at different
rates when the dishes were left
overnight in different parts of the
laboratory. (This is an observation)
Hypothesis:
Hypotheses…one more try!
Example 2: Observation – During an
experiment on plant cloning, a
scientist noticed that the root length of
plant clones varied depending on the
concentration of a hormone added to
the agar.
Hypothesis:
Variables
When you are planning an
investigation, you must identify the
variables that you are testing and the
ones that you keep constant. A
variable is any characteristic or
property able to take any one of a
range of values.
Independent variable
Dependent variable
Controlled variable
Independent Variable
Set by the person carrying out the
investigation (ex. Temperature, light
intensity, pH)
Recorded on the x axis of the graph
during data presentation
There is always only one in an
investigation
Must record proper unit
Dependent Variable
Measured during the investigation
(ex. Plant growth, heart rate etc)
Recorded on the y axis of the graph
during data presentation
There is always only one in an
investigation
Must record proper unit
Controlled Variables
Factors that are kept the same or
controlled.
List these in the method as
appropriate to your own investigation
Let’s play with variables!
Turn to page 18 in your Scientific Method packet. Look at the
picture and explanation on catalase activity and answer the
following questions:
1. Write a suitable hypothesis for this experiment:
2. Name the independent variable with the proper unit:
___________________________
3. List the equipment needed to set the independent variable,
describe how it was used:
4. Name the dependent variable with the proper unit:
___________________________
5. List the equipment needed to set the dependent variable,
describe how it was used:
6. List three variables that might have been controlled in this
experiment:
Data Collection
Design a data table to record your
results. Your data table should
clearly show the units and values of
the independent and dependent
variables. When you design your
data table, leave some room for data
processing. (IB likes to see your
math!)
Let’s practice on page 21…
Data Presentation
Graphical presentation of data provides a visual image of
trends in the data in a minimum of space. The following
are a list of characteristics of a well-done graph:
accurately shows the facts
complements or demonstrates arguments presented in the
text
has a title and labels
is simple and uncluttered
shows data without altering the message of the data
clearly shows any trends or differences in the data
is visually accurate (i.e., if one chart value is 15 and another
30, then 30 should appear to be twice the size of 15)
Constructing and reading graphs is one of the most basic
standards of the Ohio State Science Curriculum. You
must be able to do both.
Types of Graphs
I’m sure you’ve learned all about graphs in Math
class, but we are going to review them with
respect to Biological Statistics.
The most challenging part about graphing is
deciding which graph to use. Choosing the wrong
graph can obscure information and make data
more difficult to interpret.
Some examples…
Scatter Graph
Line Graph
Bar Graph
Histogram
Pie Graph
Scatter Graph
In scatter graphs, there is no manipulated
(independent) variable but the variables
are usually correlated. The points on the
graph do not need to be connected, but a
line of best fit is often drawn through the
points
The data for this graph must be continuous for
both variables.
Useful for determining the relationship between
two variables.
Let’s practice on p. 31
Line Graph
Line graphs are used when one
variable (called the independent
variable), affects another, the
dependent variable. The independent
variable is often time or the
experimental treatment. The
dependent variable is usually the
biological response.
The data for line graphs must be
continuous for both variables.
If extreme points are likely to be
important, draw a line connecting the
Line Graphs, continued…
Error Bars!
IB knocks off SIGNIFICANT points on graphs if
you do not included error bars when necessary.
Why do you need error bars, in other words,
what do they tell you?
Where error bars are large, the data are less
consistent (more variable) than in cases where the
error bars are small.
When do you need error bars?:
Error bars are used if there are calculated mean
(average) values and a measure of data spread
(standard deviation).
Line Graphs, continued…
Two curves plotted together
More than one curve can be plotted per set of
axes. This is useful when you wish to compare
two data sets together.
If the two data sets use the same measurement units
and a similar range of values use one scale and
distinguish the two curves with a key.
If the two data sets use different units and/or have a
very different range of values use two scales
Adjust scales if necessary to avoid overlapping plots.
Let’s practice on pg. 32-34
Bar Graph
The data for this graph are non-numerical
and discrete for at least one variable, in
other words, they are grouped into
separate categories. There are no
independent or dependent variables. Axes
may be reversed to give graph with the
categories on the x axis.
The data are discontinuous, so the bars do not
touch
Data values may be entered on or above the
bars
Multiple data sets can be displayed using
different colored bars placed side by side within
the same category.
Let’s practice on p. 27
Histograms
Histograms are plots of continuous data,
usually of some physical variable against
frequency of occurrence. Column graphs
are drawn to plot frequency distributions
when the data are discrete, numerical
values (1,2,3, etc). In this case, the bars do
not touch.
The X-axis usually records the class
interval. The Y-axis usually records the
number of individuals in each class interval
(frequency).
Let’s practice on p. 28…
Pie Graph
As with bar graphs, pie graphs are used
when the data for one variable are discrete
(categories) and the data for the other are
in the form of counts. A circle is divided
according to the proportion of counts in
each category. Pie graphs are:
Good for visual impact and showing relative
proportions.
Useful for six or fewer categories.
Not suitable for data sets with a very large
number of categories.
Let’s practice on p. 29
Descriptive Statistics
Mean, median, and mode
Used to highlight trends or patterns in
the data.
Frequency graphs are useful for
indicating the distribution of data.
Standard deviation and standard
error are used to quantify the amount
of spread in the data and evaluate the
reliability of estimates of the true
mean.
Mean
The average of all data entries
To calculate the mean…add up all the data
entries, and divide by the total number of
data entries.
When you DO NOT calculate a mean…
DO NOT calculate a mean from values that are
already means themselves.
DO NOT calculate a mean of ratios for several
groups of different sizes; go back to the raw
values and recalculate.
DO NOT calculate a mean when the
measurement scale is not linear; e.g. pH units
are not measured on a linear scale.
Median
The middle value when data entries
are placed in rank order.
A good measure of central tendency
for skewed distributions.
To calculate the median…
Arrange the data in increasing rank
order.
Identify the middle value.
For an even number of entries, find the
mid point of the tow middle values
Mode
The most common data value.
Suitable for biomodal distributions
and qualitative data.
To calculate the mode…
Identify the category with the highest
number of data entries using a tally chart
or a bar graph.
Range
The difference between the smallest
and largest data values.
Provides a crude indication of data
spread.
To calculate the range…
Identify the smallest and largest values
and find the difference between them
Standard Deviation
A frequently used measure of the
variability (spread) in a set of data.
It is usually presented in the form of
mean +/_ standard deviation.
For normally distributed data, about
68% of all values lies within +/- 1
standard deviation of the mean. This
rises to about 95% for +/- 2 standard
deviations.
Let’s practice…p. 40 (1-2)
T-test
Commonly used test when comparing
two sample means, e.g. means for a
treatment and a control in an
experiment, or the means of some
measured characteristic between two
animal or plant populations.
A good test for distinguishing real but
marginal differences between samples.
A two-group test, in other words, you
must have only two sample means to
compare.
Using T-tests
Used to determine if two populations
or two groups are the same or not.
Null hypothesis: the two comparison
groups are the same.
You compare the means (the average
of all data entries) of the two groups
such as small but distinguishing
differences between the samples.
Using T-tests, ctd..
You must have only two sample
means to compare.
You must assume that the data have
normal distribution and the scatter of
the data points is similar for both
samples.
General steps in Student T-test
Step 1- calculate number of values
(n), mean (x), and standard deviation
(s)
Set up and state the null hypothesis.
Decide if you test is one or two tailed
(can differ in one direction or both
directions (+/-)
Calculate the t statistic (usually done
by a spreadsheet)
General steps in Student T-test,
ctd…
Determine the degrees of freedom (na + nb
– 2)= df
Consult a t table to determine your P value
(probability level)
Determine if your null hypothesis is
accepted or rejected by comparing your
calculated t value with the appropriate
number of degrees of freedom to the value
in your t chart. If you value is higher, your
null hypothesis will be rejected.