# Textbook notes - applicable to spring term as well

CHAPTER ONE

INTRO TO STATISTICS

1.1

Statistics: set of standardized mathematical procedures used to organize, summarize

& interpret info.

1.2

population: set of all individuals of interest in a particular study

sample: set of individuals WITHIN a population, usually selected to represent the

population in a research study.

** can also refer to scores that correspond to individuals rather than the indiv.

themselves**

Once a study is done on a sample, the goal is to generalize the results back to the

poplulation.

variable: characteristic or condition, values for different individuals or over time.

Data: measurements or observations datum = 1 data set = collection of “

Score / raw score: common words for datum

Parameter : a characteristic that describes the population

•usually numerical value

•usually derived from measurements of individuals within population

o

ex: avg. reading score for *all 4th-graders in Texas*

**= population

\ \ \ ~~~~“POPULATION PARAMETER”~~~~/ / /

Statistic: a characteristic that describes a sample

•usually numerical value

•usually derived from measurements of individuals within sample

\ \ \ ~~~~~“SAMPLE STATISTIC”~~~~~/ / /

DESCRIPTIVE STATISTICS: used to summarize, organize&simplify data (raw

scores)

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INFERENTIAL STATISTICS: sample data is used to make general

statements/reach generalized conclusions about the population from which it is

selected.

Sampling error: discrepancy between sample statistic & corresponding population

parameter.

Ex: sample proportion error.

STEP-BY-STEP EXAMPLE OF STATISTICS IN IN RESEARCH

Population: first-grade children

Sample A: first-graders taught by method A

Sample B: first-graders taught by method B

1.a.Experiment: compare 2 teaching methods

b.Data: Collect test scores for students in each sample

2.Descriptive Statistics: organize & simplify

a. find average scores for each sample

b.graph

3.Inferential Statistics: interpret results

a.Compare averages: calculate difference

b.Decide whether the difference in data is due to sampling error or a

difference in the 2 methods of teaching.

1.3 DATA STRUCTURES, RESEARCH METHODS

to establish existence of relationships, measurement of two variables is necessary.

There are two data structures

I. CORRELATIONAL METHOD: measuring 2 variables for each individual, to

determine whether there is a relationship between them. Variables are not

manipulated, one group of individuals is studied

II. EXPERIMENTAL & NONEXPERIMENTAL: comparing multiple groups of

scores. 1 variable is used to define a group, and the second is measured.

Ex: 1 group is shown violent movies, the other is shown romantic comedies,

then both groups are tested for aggressive behavior.

Variable 1 = type of movie

Variable 2 = aggression

•EXPERIMENTAL METHOD: demonstrate causal relationship between two

variables

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## Document Summary

Statistics: set of standardized mathematical procedures used to organize, summarize. 1. 2 population: set of all individuals of interest in a particular study sample: set of individuals with in a population, usually selected to represent the population in a research study. ** can also refer to scores that correspond to individuals rather than the indiv. themselves** Once a study is done on a sample, the goal is to generalize the results back to the poplulation. variable: characteristic or condition, values for different individuals or over time. data: measurements or observations datum = 1 data set = collection of . score / raw score: common words for datum. parameter : a characteristic that describes the population: usually numerical value, usually derived from measurements of individuals within population, ex: avg. reading score for *all 4th-graders in texas* \ \ \ ~~~~ population parameter ~~~~ / / /