Class Notes (1,100,000)

CA (630,000)

UTSC (30,000)

Psychology (8,000)

PSYB04H3 (100)

Connie Boudens (100)

Lecture 10

Department

PsychologyCourse Code

PSYB04H3Professor

Connie BoudensLecture

10This

**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

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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

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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:

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