MAKING MEANING OUT OF DATA Statistics for IB-SL Biology.
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Transcript of MAKING MEANING OUT OF DATA Statistics for IB-SL Biology.
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MAKING MEANING OUT OF DATA
Statistics for IB-SL Biology
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There are two types of data.
QuantitativeQualitative
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There are two types of data.
Quantitative dataMeasured using a naturally occurring numerical
scale Examples
Chemical concentration Temperature Length Weight…etc.
If you give a group of students a taste test and they have to rank drinks in order of 1-10 as to which one they like, would this be quantitative data?
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There are two types of data.
Qualitative dataInformation that relates to characteristics or
description (observable qualities)Information is often grouped by descriptive
categoryExamples
Species of plant Type of insect Shades of color Rank of flavor in taste testingQualitative data can be “scored” and evaluated numerically
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Can you count EVERY ONE?!?!
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Sampling Data
Don’t have enough time or resources to measure every individual in a population.
Choose and measure a representative sample from a population.
Need to have a good SAMPLE SIZE in order to “believe” your data. (statistically significant)
Sample Size –
How many can you measure?
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Statistical analysis of a sample
Mean: is the average of data pointsRange: range is the measure of the spread
of dataStandard Deviation: is a measure of how
the individual observation of data set are dispersed or spread out around the mean
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The standard deviation tells us how tightly the data points are clustered together.
When standard deviation is small—data points are clustered very close
When standard deviation is large—data points are spread out
Statistical analysis of a sample
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Statistical analysis of a sample
We will use standard deviation to summarize the spread of values around the mean and to compare the means and spread of data between two or more sample
In a normal distribution, about 68% of all values lie within ±1 standard deviation of the mean.
This rises to about 95% for ±2 standard deviation from the mean
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Statistical analysis of a sample
Confidence Intervals (CI) We will use a CI of 95%. How many standard deviations away from the mean is this? This 95% CI will be used to measure the “significance” of
the data. We are 95% confident that the mean will be found within
this interval. What do we call data that lies outside of this?
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Statistical analysis of a sample
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Statistical analysis of a sample
Let’s imagine a scenario… You are trying to find the average height of a 5th grade
student. You measure 15 students present in one 5th grade class. You calculate the mean to be 1.6 m and the confidence
interval to be +/- 0.5m. This +/- amount represents which value? You measure a second class of 5th grade students, and then a
third class of 5th graders, etc. 95% of the time the mean should be between which two values?
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Statistical analysis of a sample
Your experiment leads you to a new question… Are 6th graders the same height as 5th graders? You should always assume the null hypothesis, or n0. This is that there is no significant difference between the
two variables. You use the same procedure to measure the height of 6th
graders. How will you know if there is enough of a difference
between the average heights of the two groups to truly be different?
Statistical significance: 95% of the data should not overlap
So, CI’s should not overlap. If they do, it is not statistically significant (not different).
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Statistical analysis of a sample
To present your data for all IA’s, you MUST use MS Excel. CI’s will be represented on graphs through the use of error
bars. These error bars represent the spread of data around the
mean.
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Statistical analysis of a sample
Classes158
160
162
164
166
168
170
161.6
168.266666666667
5th graders6th graders
Heig
ht
(cm
)
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Statistical analysis of a sample
What can you conclude when error bars do overlap?
No surprises here. When error bars overlap, you can be sure the
difference between the two means is not statistically significant.
Why? Due to chance variations!
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Statistical analysis of a sample
What can you conclude when error bars do not overlap?
When error bars do not overlap, you cannot be sure that the difference between two means is statistically significant.
The t-test is commonly used to compare these groups.
We WILL be learning about t-tests.
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Statistical analysis of a sample
What if you are comparing more than two groups?
ANOVA is a statistical test commonly used for comparing multiple groups.
We will not be using ANOVA!
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The t-test
The t-test determines whether the difference observed between the means of two samples is significant
The test works by considering the following: The size of the difference between the means of the
samples. The number of items in each sample. The amount of variation between the individual
values inside of each sample…this is known as…? The t-test always assumes what is called the null
hypothesis, or n0.This always states that there is no significant
different between the two pieces of data.
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The t-test
When a t-test is performed it returns a “p” value.We want p< 0.05When p < 0.05 this means that less than 5% of
the time the CIs of the two groups will overlap.5%...why does this sound familiar?This means that the two groups ARE
statistically different. When p > 0.05, there is a greater chance the CIs
of the two groups will overlap.This means that the two groups ARE NOT
statistically different.The difference is due to natural randomness.
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The t-test
Let’s go back to the height of the 5th and 6th graders…
Using a calculator or Excel, you determined the following value:
p = 0.117695155What question do you ask yourself? Is there a difference between the heights of the 5th
and 6th graders?
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Correlation
Correlation is a measure of the association between two factors.
The strength of the association between two factors can be measured.
An association in which all the values closely follow the trend is described as being a strong correlation.
An association in which there is much variation, with many values being far from the trend, is described as being a weak correlation.
A value can be given to the strength of the correlation, r. r = +1 a complete positive correlation r = 0 no correlation r = -1 a complete negative correlation
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Correlation
0 5 10 15 20 25
0
10
20
30
40
50
60
70
80
90
100R² = 0.695346381573607
Sunlight Intensity vs. Temperature
Sunlight (lux)
Te
mp
era
ture
(C
)
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Correlation
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Correlation