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You will go through the process of science, learning how statistics is applied to a study of saguaros. Statistics. OBSERVATION. Saguaros seem to occur in different numbers on the north versus south slope of Gates Pass. Gates Pass. www.gamineral.org/t04-gates_pass.html. RESEARCH QUESTION.

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Presentation Transcript
slide1
You will go through the process of science, learning how statistics is applied to a study of saguaros.

Statistics

observation
OBSERVATION
  • Saguaros seem to occur in different numbers on the north versus south slope of Gates Pass.

Gates Pass

www.gamineral.org/t04-gates_pass.html

research question
RESEARCH QUESTION
  • Based on the observation that saguaros seem to occur in different numbers on the north and south slopes of Gates Pass:What descriptive question (versus causal question) could you ask?
  • Descriptive: Is saguaro density affected by whether the slope faces north versus south? Only requires a count to answer.
  • Causal: Why is saguaro density affected by whether the slope faces north versus south? Requires controlled studies of many factors to answer.
literature review
LITERATURE REVIEW
  • Not needed to come up with the multiple hypotheses for this question because it is a descriptive question – there either is not an effect or there is an effect of slope direction on saguaro density.
multiple hypotheses
MULTIPLE HYPOTHESES

First, what is the null hypothesis (H0) – the one that states that there is not a cause and effect relationship?

  • H0: Saguaro density is not affected by whether the slope faces north versus south.

Second, what are the alternative hypotheses (H1 and H2 in this case)?

  • H1: Saguaro density is greater on the north-facing slope.
  • H2: Saguaro density is greater on the south-facing slope.
deductions
DEDUCTIONS
  • What evidence would you need to be convinced each hypothesis is correct or incorrect?
  • In other words, By how much would the densities have to differ for you to be convinced that the direction of the slope affects saguaro density?
  • We will come back to this later…but in “real life” you are supposed to come up with deductions before collecting any data.
tests three data sets
TESTS: Three Data Sets

Imagine that these are three possible sets of data that you could have collected by counting saguaros on a north and south slope. Note that I have kept the total number of saguaros the same (200) for each data set.

tests three examples
TESTS: THREE EXAMPLES

Here are the same data displayed in a bar graph

tentative conclusion
TENTATIVE CONCLUSION
  • For each data set (A-C) did the evidence convince you that the differences in densities were significant enough to warrant ruling out the null hypothesis (that the distribution of saguaros on the two slopes was just random) and tentatively concluding that slope did affect saguaro density?
tentative conclusion10
TENTATIVE CONCLUSION

p=probability this would happen randomly?

Perhaps it would help to know what the chance is that the distribution is random?

p=?%

p=?%

p=?%

tentative conclusion11
TENTATIVE CONCLUSION

But how do we determine the probability that the distribution is random?

STATISTICS

statistics
STATISTICS
  • If you are comparing counts, then use the chi (pronounced kie) square test.
    • Example: count of saguaros on two slopes.
  • If you are comparing averages, then use the t-test.
    • Example: comparing average height of saguaros on two slopes.
chi square test
Chi-Square Test
  • You compare the actual counts to what the expected count would be if the distribution was random.
  • In our case, with a total of 200 saguaros counted on both slopes, what would the expected distribution be if they were distributed perfectly random?
chi square test14
Chi-Square Test
  • The chi-squared test tells you the probability that the difference between observed and expected occurred by chance.
chi square test15
Chi-Square Test

Type in the numbers in the gray boxes and then hit enter

  • Use my Excel file online
tentative conclusion16
TENTATIVE CONCLUSION

p=probability this would happen randomly

Using my Excel file online, you would come up with these probabilities.

p=0.89=89%

p=0.16=16%

p=0.005=<1%

deductions17
DEDUCTIONS
  • Which brings us back to deductions.
  • What probability of being wrong are we willing to risk?
    • The worse mistake you can make in science (Type 1 error) is to conclude that the difference is due to cause and effect when it was really random.
  • Are we willing to be wrong 89% of the time? 16% of the time? Less than 1% of the time?
  • Scientists most often use 5% = p<0.05
deductions18
DEDUCTIONS
  • If slope direction does not affect saguaro density, then p > or equal to 0.05 (5%).
  • If saguaro density is greater on the north slope, then p will be less than 0.05 and saguaro density will be greater on the north slope.
  • If saguaro density is greater on the south slope, then p will be less than 0.05 and saguaro density will be greater on the south slope.
tentative conclusion19
TENTATIVE CONCLUSION
  • For data sets A&B, because p>0.05 there is no significant difference in saguaro density so slope direction unlikely to affect saguaro density.
  • For data set C, because p<0.05 saguaro density is significantly greater on the south slope so slope direction likely affects saguaro density.
this could be your table

North

slope

South

slope

Significant?

Data set A

Data set B

Data set C

No; p=0.89

No; p=0.16

Yes; p=0.005

99

90

80

101

110

120

This could be your table

Table 1. Number of saguaros per hectare on the north and south slope of Gates Pass near Tucson, AZ as counted September 1, 2009.

this could be your graph
This could be your graph

Figure 1. Number of saguaros per hectare on the north and south slope of Gates Pass near Tucson, AZ as counted September 1, 2009.

t test example
T-Test Example
  • The t-test tells you the probability that the difference between two averages is random, and considers variability within the data.
  • For example
    • H0: Slope direction does not affect saguaro height.
    • H1: Slope direction does affect saguaro height.

Gates Pass

www.gamineral.org/t04-gates_pass.html

sample data
Sample Data

Table 1. Saguaro height (in meters) on the north and south slope of Gates Pass near Tucson, AZ as counted September 1, 2009.

sample data24
Sample Data

Table 2. Average saguaro height (in meters) for 7 saguaros measured on the north and south slope of Gates Pass near Tucson, AZ on September 1, 2009.

Are these significantly different?

It depends on sample size and amount of variability in the data.

slide25

Number saguaros

Number saguaros

1.0

1.0

2.0

2.0

3.0

3.0

4.0

4.0

Height

Height (m)

Lots of variability:Probably not significantly different

South slope

average

North slope

average

x

x

x

x

x

x

x

x

x

x

x

x

x

x

North slope

average

Little variability:Probably significantly different

South slope

average

x

x

x

x

x

x

x

x

x

x

x

x

x

x

t test

Group 1

Group2

3.5

1.0

0.2

3.2

Etc.

Etc.

P value 0.272

T-Test
  • Use my Excel file online
  • Click on the t-test tab at the bottom.

TENTATIVE CONCLUSION?

slide28

If P<0.05then we tentatively conclude that there is a significant difference because there is less than 5% chance that it could have happened randomly.

slide29

If P>0.05 then we tentatively conclude that there is NOT a significant difference because there is more than 5% chance that it could have happened randomly.

t test30

Group 1

Group2

3.5

1.0

0.2

3.2

Etc.

Etc.

P value 0.272

T-Test
  • Average saguaro height on the north slope (1.5 m) is not significantly different (p=0.27) from average saguaro height on the south slope (2.4 m).

TENTATIVE CONCLUSION?

review
REVIEW
  • Before you collect data (i.e., in your proposal) you have to decide how much difference is enough to convince you that there is cause and effect going on versus just random chance.
  • Statistics (e.g., chi-square and t-test) can be used to calculate the probability that the difference is random.
  • Use p<0.05 to rule out the null hypothesis and tentatively conclude there are significant differences that suggest cause and effect.