measures of central tendency and graphs

Question 1

mean weakness

Answer 1

one rogue score (large or small) can heavily influence it

Question 2

mean strength

Answer 2

the most powerful measure of central tendency as it uses all of the data

Question 3

mode strength

Answer 3

the best measurement if you want to know how often things occur

Question 4

mode weakness

Answer 4

sometimes a data set does not have a common value and sometimes it has a lot

Question 5

median strength

Answer 5

not influenced by extreme scores

Question 6

median weakness

Answer 6

not good with using small data sets

Question 7

standard deviation strength

Answer 7

uses very value in the Data set,
not heavily distorted by extreme values and is the most sensitive

Question 8

standard deviation weakness

Answer 8

the most difficult of the measures of dispersion to calculate

Question 9

range strength

Answer 9

takes all of the data into account and is simple to calculate

Question 10

range weakness

Answer 10

if either of the 2 scores are extreme, this will be distorted. it tell us little about how spread out or clustered together the data are

Question 11

how to work out median

Answer 11

the middle number after ordering from smallest to biggest

Question 12

what is standard deviation

Answer 12

the spread of results around the mean (- a measure of dispersion)

Question 13

what does it mean if the standard deviation is more than the mean

Answer 13

its more varied
inconsistent

Question 14

what does it mean if the standard deviation is less than the mean

Answer 14

less varied
more consistent

Question 15

bar chart

Answer 15

the height of each bar represents the frequency
suitable for non-continuous data - space between bar = lack of continuity
use with categories

Question 16

what level of measurement is mean

Answer 16

interval
universal - equal units

Question 17

what level of measurement is median

Answer 17

ordinal
ranked - not equal units

Question 18

what level of measurement is mode

Answer 18

nominal
categories

Question 19

line graph

Answer 19

continuous data on x-axis

Question 20

histogram

Answer 20

continuous data
cannot draw this type of graph if data is in categories
vertical axis = frequency - starts at 0
no gaps between bars

Question 21

scattergram

Answer 21

represents data collected from correlations (naturally occurring)
doesn’t matter what axis they go on

Question 22

negative skew

Answer 22

mean is lower than median and mode

Question 23

normal distribution

Answer 23

bell-shaped curve
mean, median and code are all in the exact mid point

Question 24

positive skew

Answer 24

mean is higher than median and mode
most of data on left ()

Question 25

co-variables

Answer 25

show a naturally occurring relationship (not manipulated)

Question 26

variable

Answer 26

manipulated

Question 27

correlation coefficient

Answer 27

the strength of the relationship
-1 = perfect
-0.5 = moderate
0 = weak
+0.5 = moderate
+1 = perfect

Question 28

negative correlation

Answer 28

one increases and the other decreases

Question 29

positive correlation

Answer 29

both increase

Question 30

hypothesis for a correlation guide

Answer 30

there will be positive/ negative relationship between …..

Question 31

correlations v experiments - manipulation

Answer 31

experiment - researcher manipulates IV and DV
correlation - cannot manipulate as the variables are naturally occurring

Question 32

correlations v experiments - EVs

Answer 32

experiment - control EVs
correlations - not controlled and so a third untestable variables may be causing the relationship between the 2 variables

Question 33

correlation strength

Answer 33

P = relatively economical
E = unlike a lab study, there is no need for a controlled environment and can use secondary data
E =so correlations are less time-consuming than experiments

Question 34

correlation weakness

Answer 34

P = no cause and effect
E = correlations are often presented as casual when they only show how 2 variables are related
E = this leads to false conclusions about causes of behaviour