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IM/Stats

What is the importance of statistics?

It enables us to:

- empirically test hypotheses and theories

- evaluate information in

an objective way so we can draw conclusions about our scientific object.

IM/Stats

Describe advantages and disadvantages of four sample layout systems

Stratified random sampling is more efficient because the variance within the layer (stratum) is smaller, if the differences between layers (strata) are larger.

IM/Stats

What are the four characteristics of a scientific hypothesis?

- Must be focused on something real, that can be investigated empirically
- Can be generalized
- Can be disproven, which implies
- A conditional sentence (If…then…)

IM/Stats

What is the difference between empirical and other research?

Empirical research is a research approach that makes use of evidence-based data while non-empirical research is a research approach that makes use of theoretical data.

IM/Stats

What is a Type I Error?

When you wrongly reject H0 (null hypothesis)

IM/Stats

What is the degree of freedom?

df = n-1

1) The number of independent comparisons that can be made in a set of data.

2) The maximum number of quantities whose values are free to vary before the remainder of the quantities are determined.

IM/Stats

Which 4 characteristics must be fulfilled by a scientific hypothesis?

- testable
- falsifiable
- generalised
- based on empirical observations?

IM/Stats

What is a Type II Error?

When you wrongly accept H0 (null hypothesis)

IM/Stats

Describe the steps in performing a frequency distribution by means of Excel.

First, think about class width “every class should somehow be occupied”

Start with the lower class limit, end with the upper class limit

call up the frequency function: FREQUENCY(data;classes).

Fill in the empirical data under “Data” and the predefined classes under “classes”. Mark the cells where your frequencies are to appear and then press “F2”.

Press “Ctrl-Shift-Enter“ at the same time.

IM/Stats

Explain the Chi test for normality

The Chi-Square Test for Normality allows us to check whether or not a model or theory follows an approximately normal distribution.

Null-hypothesis: there is no difference between observed and expected (normal distribution) data.

p < 0.05 --> the observed data is significantly different from the an expected normal distribution data

IM/Stats

Please indicate 2 examples for scientific and 2 examples for non-scientific hypotheses.

SCIENTIFIC

- If the amount of investments in a country increase, the GDP will increase as well.

- Trees from lower slopes are bigger than trees from flat plateaus.

NON-SCIENTIFIC

- There are investments that increase the GDP. --> cannot be falsified.

-It is possible that a moderate thinning from below would lead to a greater total growth

IM/Stats

How can we illustrate variability in recorded data?

to measure Variability we use mainly the frequency distribution

there are other measures like sum of differences, sum of square differences, the variance, the standard diviation, the sampling distribution of the mean, the sampling error of the mean

To illustrate this we can use plots like the dot plot or a graph with probability density function

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