# The Beast of Bias (L5) Flashcards

1
Q

What are outliers?

A

Extreme scores that have a tendency to bias the parameters. They can also alter the value of the mean.

2
Q

What is the assumption of normality?

A

Refers to the fact your sampling distribution needs to be normal (NOT data). Normality can influence parameter estimation and confidence interval estimation.

3
Q

What is central limit theorem?

A

Tells us about under what conditions we should find normality. As long as sampling size is big enough (usually N=30 in theory, N=100 in practice), the distribution will be normal.

4
Q

What is homogeneity of variance?

A

Difference data points have similar variance (SDs) which are consistent across conditions.

5
Q

What is heterogeneity of variance?

A

The different data points do not have similar variance (SDs) across different data sets.

6
Q

How can we detect bias?

A

Through graphs, numbers and standardized residuals.

7
Q

How many standard deviations outside the mean does a data point have to be for it to be considered an outlier?

A

3 standard deviations.

8
Q

How can we detect normality?

A

Through graphs, box plots, P-P/Q-Q plots, skew, kurtosis and K-S tests.

9
Q

How do we correct observed problems?

A

1) Trim the data.
2) Winzorising.
3) Bootstrapping.
4) Transforming the data.

10
Q

What is the assumption of additivity and linearity?

A

Assumption that the outcome is linearly related to any predictor. Most important as if this assumption isn’t met, no others are.

11
Q

What is independence?

A

The errors in your model are not related to each others.

12
Q

What are P-P plots and Q-Q plots?

A

Test normality;
P-P plots; show the cumulative probability of a variable against the cumulative probability of a particular distribution.
Q-Q plots; same but expressed as quantiles.

Follow basic line to show normality, with minimal variance.

13
Q

What are Kolmogorov-Smirnov tests and Shapiro-Wilk tests?

A

Compare the socres in the same to a normally distributed set of scores with the same mean and standard deviation.

If non-significant; normally distributed

If significant; non-normally distributed.

14
Q

What is Zskewness?

A

(S - 0) / SEskewness

```S = skewness statistic
SEskew = std. error```

Both values found in SPSS output.

Used to assess significance of skew.

15
Q

What is Zkurtosis?

A

(K - 0) / SEkurtosis

```K = kurtosis statistic
SEkurt = std. error```

Both values found in SPSS output.

Used to assess significance of kurtosis.

16
Q

What are the significant results for Zskewness and Zkurtosis?

A

> 1.96 = p2.58 = p3.29 = p

17
Q

How do you report a K-S test?

A

D(df) = statistic

All found in SPSS output.