Class Notes (1,100,000)
CA (630,000)
UTSC (30,000)
Psychology (8,000)
PSYB04H3 (100)
Lecture 10

PSYB04H3 Lecture Notes - Lecture 10: Statistical Inference, Anagram, Null Hypothesis


Department
Psychology
Course Code
PSYB04H3
Professor
Connie Boudens
Lecture
10

This preview shows pages 1-3. to view the full 18 pages of the document.
Data Analysis
2 types:
o Descriptive (summarizes data)
Describes data you have
o Inferential (uses data to draw conclusions about population)
o Below:
Descriptive statistics summaries and describes the data collected
First thing with a data set, give basic descriptor's of what data looks like but only that
includes in the sample
One of the basic sets of measure of central tendency
Basically mean median and mode
The small chart is descriptive statistics on excel
On the right side, see mean, median , and mode
Measure of variability is how spread out the data is
Includes range (top score minute bottom score)
Measures of relative standing tells you where one point is relative to other ones
Ex. Percentile. Standardize tests also show percentile. Ex. 97%, better then 97%
of people who took it
Tells you were your score is compared to other scores
Measures of relationships is correlation coefficient
Table shows whether a correlation is strong, moderate, or negligible
Graphic display is also descriptive
Graphic displays that show people what the data looks like
find more resources at oneclass.com
find more resources at oneclass.com

Only pages 1-3 are available for preview. Some parts have been intentionally blurred.

Measures of Central Tendency
Mean
o Arithmetic average
o Total all data points and divide by number of data points
Median
o Mid point
o Data point where 50% fall below and 5 % data points fall above
o Some data can calculate this some cant
o Ex. Males and females: is nominal data
o Mean often used as measure of central tendency for income
Because high figures will pull the figure to high side making it look like people make
more
That's why media should be used for this
Mode
o Most frequently occurring value
High point of frequency distribution
o Does not consider distribution of all scores
Could be a distribution spread out, or narrow, could have data points or not
Could be bimodal: have 2 scores
Ex. In exams a lot of people score in C's then dip then high B's
Measures of Variability
Mean for height in both teams the same
But a lot of variability in green team vs white team
There's additional data in adtion to avergae hight that youd want to understand
o Would want to see how spread out they are
find more resources at oneclass.com
find more resources at oneclass.com

Only pages 1-3 are available for preview. Some parts have been intentionally blurred.

Definitions: Measured of Variability
Range:
o Difference between highest and lowest score
o Highest score subtracted by lowest score
Variance:
o The average variability of scores [around the mean aka arithmetic] average]
Standard Deviation: [MOST IMPORTANT THING TO KNOW]
o Square root of variance
By doing square root, see original units of measurement
o The average dispersion or deviation of scores around the mean (in original score units)
o Shows you how spread the scores are around the mean
With small SD, near the mean
But if larger, will have a range of scores that's farther away from SD
o SD tells you if the measure picks up variability in the population
Inferential Statistics
Descriptive is describe
Inferential Statistics
Used to go beyond the data obtained from your sample, and make statements about the
population.
o Ex. In a opinion poll around political preference.
Pollsters want to know how the sample relates to the larger population
Based on that, people make decisions about a lot of things
o Same thing in university or research lab setting
o Concern with if they can extrapolate to larger population
Has to do with internal validity but mostly Inferential statistics
Inferential statistics are used to draw inferences about a population from a sample.
o From data, infer certain things form which the sample was drawn
Two main methods used in inferential statistics:
find more resources at oneclass.com
find more resources at oneclass.com
You're Reading a Preview

Unlock to view full version