Statistics: Important concepts - hypotheses, significance and choosing the right test Flashcards

1
Q

What is the variance?

A

the sum of all the squared differences divided by the number of samples we have ( - 1)

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2
Q

What is the standard deviation?

A

The average difference from the mean

square root of the variance

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3
Q

What is a property of the normal distribution?

A

The concept that 95% of the data lies within 2 standard deviations of the mean.

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4
Q

What is the standard error or the mean?

A

A measure of the precision of the estimate

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5
Q

How do you calculate standard error of the mean?

A

𝑆𝐸𝑀 = 𝑆𝐷/Root(𝑛)

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6
Q

how do you calculate the 95% confidence limit of a mean?

A

π‘†π‘Žπ‘šπ‘π‘™π‘’ π‘šπ‘’π‘Žπ‘› Β±(1.96 ×𝑆𝐸𝑀)

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7
Q

What are confidence intervals?

A

Confidence intervals are an indication of how reliable our estimate is, given the data we have

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8
Q

What are confidence levels usually set at?

A

95, 90, 99%

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9
Q

What are P-values

A

P-values were devised to indicate how unlikely our result would be IF IN FACT THERE IS NO REAL EFFEC

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10
Q

What is the null hypothesis?

A

A statement of equality usually formed first to state the their is potentially no difference/ no relationship between variables.

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11
Q

What is the alternative hypothesis?

A

A statement of inequality stating that there is a difference between 2 variables or a difference in relationship. This can either be directional or non-directional

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12
Q

What is a non-directional hypothesis?

A

reflects a difference between groups but the direction of difference is not specified (eg. increase or decrease)

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13
Q

What is a directional hypothesis?

A

this reflects a difference between the groups , and the direction

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14
Q

What is the significance level?

A

The level of evidence we require to prove that there is something going on and to conclude the hypothesis and to accept or reject the null hypothesis.

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15
Q

What is a type 1 error?

A

When we reject the null hypothesis when it is true.

We conclude that there is an effect when there truly isn’t one.

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16
Q

What is a type 2 error?

A

We do not reject the null hypothesis when it is false, by concluding that there is not enough evidence of an effect when one truly exists.

17
Q

What is the P-value

A

the probability of obtaining the result, or something more extreme, if the null hypothesis were true

(The smaller the p-value, the greater the evidence against the null hypothesis)

18
Q

What is a parametric test?

A

based on the assumption that you data follows a determined distribution

19
Q

What is a non-parametric test?

A

if you can’t assume the data follow any particular distribution, but they are less potentially less powerful