Statistics

Chapter 6

The beast of bias

## What is bias?

Bias: the summary information is at odds with the objective truth.

An unbiased estimator: one estimator that yields and expected value that is the same thing it is trying to estimate.

We predict an outcome variable from a model described by one or ore predictor variables and parameters that tell us about the relationship between the predictor and the outcome variable.

The model will not predict the outcome perfectly, so for each observation there is some amount of error.

Statistical bias enters the statistical process in three ways:

- things that bias the parameter estimates (including effect sizes)
- things that bias standard errors and confidence intervals
- things that bias test statistics and p-values

## Outliers

An outlier: a score very different from the rest of the data.

Outliers have a dramatic effect on the sum of squared error.

If the sum of squared errors is biased, the associated standard error, confidence interval and test statistic will be too.

## Overview of assumptions

The second bias is ‘violation of assumptions’.

An assumption: a condition that ensures that what you’re attempting to do works.

If any of the assumptions are not true then the test statistic and p-value will be inaccurate and could lead us to the wrong conclusion.

The main assumptions that we’ll look at are:

- additivity and linearity
- normality of something or other
- homoscedasticity
**/**homogeneity of variance - independence

**Additivity and linearity **

The assumption of additivity and linearity: the relationship between the outcome variable and predictor is accurately described by equation.

The scores on the outcome variable are, in reality, linearly related to any predictors. If you have several predictors then their combined effect is best described by adding their effects together.

If the assumption is not true, even if all the other assumptions are met, your model is invalid because your description of the

... Interested? Read the instructions below in order to read the full content of this page.## Access options

The full content is only visible for Logged in World Supporters.

More benefits of joining World Supporter

- You can use the navigation and follow your favorite supporters
- You can create your own content & add contributions
- You can save your favorite content and make your own bundles
- See the menu for more benefits

Full access to all pages on World Supporter requires a JoHo membership

- For information about international JoHo memberships, read more here.

**Support JoHo and support yourself by becoming a JoHo member **

**Support JoHo and support yourself by becoming a JoHo member**

**Become a Member**

**Become a Member**** **

** **

**for free**to follow other supporters, see more content and use the tools**for a small donation by becoming a member**to see all content

**Why create an account?**

- Your WorldSupporter account gives you access to all functionalities of the platform
- Once you are logged in, you can:
- Save pages to your favorites
- Give feedback or share contributions
- participate in discussions
- share your own contributions through the 11 WorldSupporter tools

# Discovering statistics using IBM SPSS statistics by A. Field (5th edition) a summary

This is a summary of the book "Discovering statistics using IBM SPSS statistics" by A. Field. In this summary, everything students at the second year of psychology at the Uva will need is present. The content needed in the thirst three blocks are already online, and the rest

...
## Add new contribution