Stats and experimental design

Question 1

Define correlational studies

Answer 1

Observing what naturally goes on in the world without directly interfering with it

Question 2

Define experimental studies

Answer 2

one or more variable is systematically manipulated to see their effect (alone or in combination) on an outcome variable. Statements can be made about cause and effect

Question 3

Define longitudinal studies

Answer 3

This term implies that data come from people at different age points, with different people representing each age point.

Question 4

What are the 4 rules about “cause”?

Answer 4

1. Cause is the producer of an effect, while an effect is produced by a cause.
2. The cause can be a person, object, situation, or event that can result in something, while an effect is the result of the actions of the person or the outcome of some chain of events that have happened.
3. The cause will in a way explain the reason why the effect happened in the first place.
4. The cause naturally precedes an effect, while the effect will always follow it.

Question 5

What is a simple hypothesis?

Answer 5

Where there is a relationship between two variables one is called independent variable or cause and other is dependent variable or effect.

Question 6

What is a complex hypothesis?

Answer 6

Where a relation between variables exists and there is more than one dependent variable.

Question 7

What is an empirical hypothesis?

Answer 7

When the theory is put to test using actual observation and experiment

Question 8

What is a null hypothesis?

Answer 8

No relationship between dependent and independent variables

Question 9

What is an alternative hypothesis?

Answer 9

The opposite of a null hypothesis - when there are multiple hypotheses and one is selected which more workable and most efficient

Question 10

What is a logical hypothesis?

Answer 10

When the hypothesis is verified logically. The four cannons are: agreement, disagreement, difference and residue

Question 11

What is a statistical hypothesis?

Answer 11

When the hypothesis can be verified statistically. Will always be regarded as statistical regardless of it being logical or illogical

Question 12

Continuous data - interval variable

Answer 12

The difference between 1 and 2 is equivalent to the difference between 99 and 100

Question 13

Continuous data - ratio variable

Answer 13

The same as an interval variable, but the ratios of scores on the scale must also make sense

Question 14

Categorical data - binary variable

Answer 14

There are only two categories e.g. dead or alive/sink or swim/0 or 1

Question 15

Categorical data - Nominal variable

Answer 15

There are more than two categories e.g. omnivore, vegetarian, vegan, or fruitarian.

Question 16

Categorical data - Ordinal variable

Answer 16

The same as a nominal variable but the categories have a logical order, e.g. whether people got a fail, a pass, a merit or a distinction in their exam.

Question 17

Quantitative data

Answer 17

Measurable/objective measures on numeric value

Question 18

Qualitative data

Answer 18

Pain indices; subjective assessments of “condition”; Morphotypes

Question 19

Measurement error

Answer 19

The discrepancy between the actual value we’re trying to measure, and the number we use to represent that value
e.g. You weigh 75 kg. You stand on your bathroom scales and they say 80 kg. The measurement error is 5 kg.

Question 20

Index scatterplot

Answer 20

A simple plot () of data points.

Can be used to detect “runs” or can be plotted in a sorted form.

Question 21

Frequency distribution/histogram

Answer 21

A bar graph where all the bars touch. Values of observations on x and a bar showing how many times each value occurred in the data set.

More continuous than a bar graph

Question 22

Dotplot

Answer 22

A simple stacking of dots at each measure - histogram but dots that don’t touch rather than bars that touch

Question 23

Boxplot

Answer 23

Displays the interquartile ranges, the mean and the median

Question 24

Calculate the Z-score

Answer 24

Z = (sample(x) - mean(xbar))
__________________
standard deviation (s)

Question 25

Define kurtosis and what types there are

Answer 25

Kurtosis = the 'heaviness' of the tails
Leptokurtic = heavy tails (thin peaked line)
Mesokurtic = middle
Platykurtic = lights tails (wide flat line)

Question 26

What is a skew and how would a positive and a negative skew be describe?

Answer 26

The skew is the symmetry of the distribution. A positive skew has bunched data at the low values with the tail pointing to the high values and a negative skew vice versa

Question 27

How to calculate the degrees of freedom

Answer 27

Degrees of freedom is the sample size (n) minus the number of parameters estimated from the data
(usually df = n - 1)

Question 28

Calculate variance

Answer 28

variance = sum of squares/degrees of freedom

Question 29

What is a student’s t-distribution and how does it relates to parameter estimation

Answer 29

Student’s t test is used for comparing two sample means, where the samples are independent, the variances are constant, and errors are normally distributed. The test statistic is the number of standard errors by which the two sample means are separated

Question 30

Describe a Type I Error

Answer 30

Occurs when we believe that there is a genuine effect in our population when, in fact, there isn’t. The probability is the α-level (usually .05)

Question 31

Describe a Type II Error

Answer 31

Occurs when we believe that there is no effect in the population when, in reality, there is. The probability is the β-level (often .2)

Question 32

What are true positives and true negatives?

Answer 32

If it is said that there is an effect on the population and there in-fact is, this is a true positive.

This is vice versa for a true negative.

Question 33

Relative Risk Ratio equation

Answer 33

(a/(a+b)) / (c/(c+d))

Question 34

Odds Ratio equation

Answer 34

(a/b) / (c/d)

use when any of the values are below 30

Question 35

Odds Risk Probability value meanings

Answer 35

OR < 1 = event less likely in group 1
OR = 1 = event equally as likely in both groups
OR > 1 = event more like in group 1

Question 36

Relative Risk Ratio value meanings

Answer 36

RR = 0 = none of the cases in group 1 had the event occur while x number of cases in group 2 had the event occur
RR = 1 =  neutral result: the chance of an event occurring for one group is the same for an event occurring for the other group

Question 37

Sum of squares (SS)

Answer 37

(X1 - Xbar)*

Question 38

Variance

Answer 38

(X1 - Xbar)* / (n-1)

Question 39

Covariance

Answer 39

(X1 - Xbar)(Y1 - Ybar) + (X2 - Xbar)(Y2 - Ybar) + (X3 etc) / n -1

Question 40

Standard Deviation (SD/S)

Answer 40

sqrt ( (X1 - Xbar)* / (n-1) )

Question 41

Standard Error (SE)

Answer 41

s / sqrt(n)

Question 42

Confidence Interval (CI)

Answer 42

Xbar +/- t ( s/sqrt(n) )

Question 43

Chi test

Answer 43

SUM OF ( (O-E)* / E )

where O = observed frequency and E = expected frequency

Question 44

Mean Square (MS)

Answer 44

sum of squares (ss) / degrees of freedom (df)

Question 45

F-value (F)

Answer 45

mean square of row 1 / mean square over row 2