SES - Statistical Tests of Differences

Question 1

What are interpreting results based on? Example?

Answer 1

Probability e.g. odds of winning a race.

Question 2

What are interpreting results used for?

Answer 2

To interpret facts e.g. result of race.

Question 3

Discussions form experiments can be what? Example? What must they be based on?

Answer 3

Black/white/shades of grey e.g. coaches discussion of the result, but must be based on evidence.

Question 4

In relation to variables and stats tests of differences, there can be more than 1 what?

Answer 4

Independent variable.

Question 5

What is a hypothesis?

Answer 5

A precise statement about the outcome of an experiment, based on the theory.

Question 6

What 2 hypotheses does every theory have?

Answer 6

1. ) Alternate hypothesis.

2. ) Null hypothesis.

Question 7

HA? HO?

Answer 7

Alternate hypothesis.

Null hypothesis.

Question 8

Alternate hypothesis? Null hypothesis?

Answer 8

Positive and according to theory.

Negative and contradicts the theory.

Question 9

Experimental study?

Answer 9

A scientist actively manipulates/interferes - Manipulates an independent variable and measures the responses of the dependent variable.

Question 10

Research process?

Answer 10

1. ) Research question.

2. ) Hypotheses.

Question 11

Statistical inference?

Answer 11

Process of drawing conclusions about the population based upon the sample data.

Question 12

The larger the difference in results/standard deviation…

Answer 12

The more confident we are that they come from different populations.

Question 13

P-value? What is it known as?

Answer 13

Describes the extent to which the observations were due to chance and systematic effects.
Known as the probability statistic.

Question 14

When p = 0.10, chance %? systematic effects %?

Answer 14

10% chance of error.

90% systematic effects.

Question 15

Critical p-value for significance and accepting the alternate hypothesis? Chance of error and systematic effect %?

Answer 15

Typically 0.05 in science.
5% chance of error.
95% due to systematic effect.

Question 16

HO true and HO accepted?

Answer 16

Null hypothesis is accepted.

Question 17

HO true and HA accepted?

Answer 17

Type I error.

Question 18

HA true and HO accepted?

Answer 18

Type II error.

Question 19

HA true and HA accepted?

Answer 19

Alternate hypothesis is accepted.

Question 20

Type I error example?

Type II error example?

Answer 20

1 = Telling a man he's pregnant.
2 = Telling an obviously pregnant woman she's not pregnant.

Question 21

What descriptive stats would you use for 2 samples of data in relation to stats tests of differences?

Answer 21

Means.

Standard deviations.

Question 22

What is the objective for statistical tests of differences?

Answer 22

To determine whether the difference between the 2 means is large enough to reflect a real difference between the 2 populations from which the samples are drawn.

Question 23

Why use a statistical test?

Answer 23

To help the researcher reach an objective decision about the data they have collected.

Question 24

What is a problem with using statistical tests of differences?

Answer 24

2 different samples from the same population would give 2 slightly different means and SD’s because of the variance within that population.

Question 25

Chance difference?

Answer 25

Differences in the means of 2 samples randomly selected from the same population.

Question 26

What does it mean when T = 0 in a t-test?

Answer 26

There’s no difference in means.

Question 27

The larger the T value, the larger the…

Answer 27

Difference between means.

Question 28

What does a t-test tell you?

Answer 28

Tells you how significant the differences between groups are.
Tells you if those differences could have happened by chance.

Question 29

What should you consider when constructing hypotheses?

Answer 29

1. ) Difference or relationship?
2. ) Dependent variable?
3. ) Independent variable and the levels of the independent variable?

Question 30

What does an independent sample t-test measure?

Answer 30

Difference between means of 2 samples made up of different people.

Question 31

Assumptions of independent sample t-test?

Answer 31

1. ) Interval/ratio data.
2. ) Data must be normally distributed (i.e. skewness & kurtosis)
3. ) Samples must be randomly selected from a population (i.e. statistical inference)
4. ) Variance from each sample must be around equal.

Question 32

How can you check if the variance from each sample is equal when carrying out an independent sample t-test?

Answer 32

If 1st SD is 2x > 2nd SD = violated.

Question 33

Levene’s test?

Answer 33

Inferential statistic used to assess the equality of variances for a variable calculated for 2 or more groups.

Question 34

Formula for a t-test?

Answer 34

T = X1 - X2 / SED

Question 35

X1? (formula for t-test)

Answer 35

Mean of sample 1.

Question 36

X2? (formula for t-test)

Answer 36

Mean of sample 2.

Question 37

SED? (formula for t-test)

Answer 37

Standard error of the difference.

Question 38

One-tailed test of difference? Example?

Answer 38

Investigator knows whether difference will be higher or lower than 2nd sample e.g. endurance runners’ VO2max vs sedentary subjects.

Question 39

Two-tailed test of differences? Example?

Answer 39

Investigator does not know whether difference will be higher or lower than 2nd sample e.g. football players vs hockey players VO2max.

Question 40

Before carrying out a t-test which assumptions should you check?

Answer 40

Interval or ratio data.
Random sampling.
Normality.

Question 41

Writing result of “identifying if anticipation performance is significantly different between a group of club and a group of recreational tennis players” into a lab report example?

Answer 41

There was no significant difference between club players and recreational players’ anticipation scores, t(x) = x, p(>/