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Paired |Design |- |correct |answer |✔Both |treatments |are |applied |to |every |sampled |unit.
Two-Sample |Design |- |correct |answer |✔Each |treatment |group |is |composed |of |an |independent, |random |sample |of |units.
Paired |t-test |- |correct |answer |✔Used |to |test |a |null |hypothesis |that |the |mean |difference |of |paired |measurements |equals |a |specified |value.
Paired |t-test |Assumptions |- |correct |answer |✔1) |The |sampling |units |are |randomly |sampled |from |the |population.
- |The |paired |differences |have |a |normal |distribution |in |the |population.
Standard |Error |of | Y₁ - Y₂ |- |correct |answer |✔ √ s²p((1/n¹)+(1/n ₂ ))
Pooled |Sample |Variance |- |correct |answer |✔s²p=(df ₁ s ²₁ +df ₂ s ²₂ )/(df ₁ +df ₂ ) |The |average |of |the |variances |of |the |samples |weighted |by |their |degrees |of |freedom.
Two-Sample |t-test |- |correct |answer |✔The |simplest |method |to |compare |the |means |of |a |numerical |variable |between |two |independent |groups.
t=(Y₁ - Y₂)/SEy₁ - y₂
Two-Sample |t-test |Assumptions |- |correct |answer |✔1) |Each |of |the |two |samples |is |a |random |sample |from |its |population.
|The |numerical |variable |is |normally |distributed |in |each |population.
|The |standard |deviation |(and |variance) |of |the |numerical |variable |is |the |same |in |both |populations.
Welch's |Approximate |t-test |- |correct |answer |✔Compares |the |means |of |two |groups |and |can |be |used |even |when |the |variances |of |the |two |groups |are |not |equal.
F-test |- |correct |answer |✔Tests |whether |the |variances |of |two |populations |are |equal. |F=s ²₁ (larger |sample |variance)/s ²₂ |(smaller |sample |variance)
F-test |Assumptions |- |correct |answer |✔1) |Both |samples |are |random |samples
- |The |numerical |variable |is |normally |distributed |within |both |populations.
Levene's |Test |- |correct |answer |✔Tests |the |difference |between |the |variances |of |two |or |more |populations.
Levene's |Test |Assumptions |- |correct |answer |✔1) |Both |samples |are |random |samples
- |The |distribution |of |the |variable |is |roughly |symmetrical |in |both |populations.
Four |Alternative |Options |for |Analyzing |Data |that |do |not |meet |assumptions |- |correct |answer |✔1) |Ignore |the |violations |of |assumptions
- |Transform |the |data
Arcsine |Transformation |- |correct |answer |✔p'=arcsin[ √ p], |used |almost |exclusively |with |data |that |are |proportions
Square-root |Transformation |- |correct |answer |✔Y'= √ Y+(1/2), |often |used |when |the |data |are |counts, |such |as |number |of |mates |required, |number |of |eggs |laid, |number |of |bacterial |colonies...
Square |Transformation |- |correct |answer |✔Y'=Y², |used |when |frequency |distribution |of |the |data |is |skewed |left
Antilog |Transformation |- |correct |answer |✔Y'=e^Y, |alternative |to |square |transformation
Reciprocal |Transformation |- |correct |answer |✔Y'=(1/Y), |use |when |data |are |skewed |right
Nonparametric |Method |- |correct |answer |✔Makes |fewer |assumptions |than |standard |parametric |methods |do |about |the |distributions |of |the |variables. |Usually |based |on |ranks |of |the |data |points |rather |than |the |actual |values |of |the |data.
Parametric |Method |- |correct |answer |✔Makes |assumptions |about |the |distributions.
Sign |test |- |correct |answer |✔A |nonparametric |method |that |can |be |used |in |place |of |the |one-sample |t-test |or |the |paired |t-test |when |the |normality |assumption |of |those |tests |cannot |be |met. |Assesses |whether |the |median |of |a |population
|equals |a |null |hypothesized |value. |It |makes |no |assumptions |about |the |distribution |of |the |measurement |in |the |population.
Wilcoxon |signed-rank |test |- |correct |answer |✔Improvement |on |sign |test, |retains |information |about |magnitudes |(how |far |below |or |above |the |hypothesized |median |each |data |point |lies)
Assumes |that |the |distribution |of |measurements |in |the |population |is |symmetrical.
Mann-Whitney |U-test |- |correct |answer |✔Can |be |used |in |place |of |the |two- sample |t-test |when |the |normal |distribution |assumption |cannot |be |met, |doesn't |require |as |many |assumptions. |Compares |the |distributions |of |two |groups. |(Assumes |they |have |the |same |shape)
Permutation |Test |- |correct |answer |✔Generates |a |null |distribution |for |the |association |between |two |variables |by |repeatedly |and |randomly |rearranging |the |values |of |one |of |the |two |variables |in |the |data.
How |to |generate |a |null |distribution |of |possible |values |of | Y₁ - Y₂ |using |permutation |- |correct |answer |✔1) |Create |a |permuted |set |of |data |in |which |the |values |of |the |response |variables |are |randomly |reordered.
|Calculate |the |measure |of |association |for |the |permuted |sample.
|Repeat |the |permutation |process |many |times-at |least | 1000 |or |more.
Permutation |Test |Assumptions |- |correct |answer |✔1) |Random |samples
- |For |permutation |tests |that |compare |means |or |medians |between |groups, |the |distribution |of |the |variable |must |have |the |same |shape |in |every |population.
Balanced |Experimental |Design |- |correct |answer |✔All |treatments |have |equal |sample |size.
Blocking |- |correct |answer |✔The |grouping |of |experimental |units |that |have |similar |properties. |Within |each |block, |treatments |are |randomly |assigned |to |experimental |units.
Randomized |block |design |- |correct |answer |✔Like |a |paired |design |but |for |more |than |two |treatments.
Factor |- |correct |answer |✔A |single |treatment |variable |whose |effects |are |of |interest |to |the |researcher.
Factorial |design |- |correct |answer |✔Investigates |all |treatment |combinations |of |two |or |more |variables. |This |can |measure |interactions |between |treatment |variables.
Interaction |- |correct |answer |✔Between |two |(or |more) |explanatory |variables |means |that |the |effect |of |one |variable |depends |upon |the |state |of |the |other |variable.
Matching |- |correct |answer |✔Every |individual |in |the |treatment |group |is |paired |with |a |control |individual |having |the |same |or |closely |similar |values |for |the |suspected |confounding |variables.
ANOVA |- |correct |answer |✔Analysis |of |Variance; |Compares |means |of |multiple |groups |simultaneously |in |a |single |analysis.
Group |mean |square |- |correct |answer |✔part |of |ANOVA; |represents |variation |among |the |sampled |individuals |belonging |to |different |groups. |It |will |on |average |be |similar |to |the |error |mean |square |if |population |means |are |equal.
Error |mean |square |- |correct |answer |✔part |of |ANOVA; |the |pooled |sample |variance, |a |measure |of |the |variation |among |individuals |within |the |same |groups.
ANOVA |Assumptions |- |correct |answer |✔(same |as |two-sample |t-test, |except |that |it |has |to |hold |true |for |all |k |groups)
|The |measurements |in |every |group |represent |a |random |sample |from |the |corresponding |population.
|The |variable |is |normally |distributed |in |each |of |the |k |populations.
|The |variance |is |the |same |in |all |k |populations
Kruskal-Wallis |test |- |correct |answer |✔A |nonparametric |method |based |on |ranks, |the |equivalent |to |the |Mann-Whitney |U-test |when |there |are |more |than |two |groups.
Planned |comparison |- |correct |answer |✔A |comparison |between |means |planned |during |the |design |of |the |study, |identified |before |the |data |are |examined.
Spearman's |rank |correlation |- |correct |answer |✔Measures |the |strength |and |direction |of |the |linear |association |between |the |ranks |of |two |variables.
Measurement |error |- |correct |answer |✔The |difference |between |the |true |value |of |a |variable |for |an |individual |and |its |measured |value.
