Data analysis 3 Flashcards

1
Q

Formulating a null hypothesis

A

There is no statistical significant difference between a serum testosterone in male athlete with conc of greater than 50 microgram/ millilitre and male athlete sprinting speed of male athlete serum conc of less than 50 mp/ml

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2
Q

Is the difference observed significant.

A

p<a>a not significant difference</a>

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3
Q

Comparing mean value, standard deviation , p value(parametric data).

A

bigger the difference in mean = smaller the. p value (data is significant)
if mean and SD are same between two groups but the p value is different, the group with smaller P value had more samples (more likely to find significant difference in a larger data)

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4
Q

comparing non parametric data by running a mann whitney U test

A
  • the difference in mean doesn’t show significant data because non parametric tests don’t compare data to a mean.
  • they rank data and look at difference between two numbers , so you dont look at data with big difference in numbers but look at numbers closer to each other so they will have a smaller difference so smaller p value showing significance.
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5
Q

to identify parametric and non parametric data what graph do you use

A
  • histogram an QQ plot
  • QQ plot shows if there is normal distribution there should be spread of data along line of best fit.
  • normal bell curve(around histogram) = parametric
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6
Q

p value and histogram that shows significant deviation to be paired or unpaired , parametric or non parametric

A
  • non parametric : doesn’t follow normal distribution
  • paired bc the same group of people used before and after
  • so you uses wilcoxon signed Rank
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7
Q

What are null and alternative hypothesis tested by ANOVA in the study

A
  • looks at any difference between any different groups
  • ANOVA null: there will be no significant difference between any groups
  • ANOVA alternative : there will be a significant difference between the groups
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8
Q

Anova test result

A
  • lots of numbers to show the computer system test is working but the only thing you are interested in is the p value
  • p value > alpha (0.05), accept null , no significant difference
  • p <a></a>
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9
Q

Two way ANOVA

A
  • level of depression of patients that have anxiety or not
  • and is there a difference of patients treated with those different drugs
  • interaction effect: between patients with anxiety or not and efficacy of drug
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10
Q

Categorical vs continuous variables

A

categorical : one or the other , male or female

continueos: take any value, height, weight.

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11
Q

fishers exact test:

A

tests if there are non random associations between two categories.

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12
Q

How does chi-squared test work?

A

compare one group with the rest of the different group,
so E vs A , E vs B, E vs C, E vs D
work out p value for each one

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13
Q

Why bonferroni correction is limited?

A
  • dont wanna do too many comparison bc you are more likely to get error
  • reduce false positive (type 1 error)
  • Tukey’s and Dunnett’s reduce error rates mathematically.
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