Lecture 6

Question 1

What can stats provide?

Answer 1

provide objective criteria for evaluating hypothesis, synthesize information, help detect patterns in data, help to critically evaluate arguments

Question 2

what stats cannot provide?

Answer 2

tell the truth, compensate for poor design,

indicate clinical significance

Question 3

examples of narrative data

Answer 3

gender, social status, profession, colour

Question 4

numerical measurements examples

Answer 4

lab results, vital signs

Question 5

what is a population

Answer 5

A population is any entire collection of people, animals, plants, or things from which we may collect data. It is the entire group we are interested in, which we wish to describe or draw conclusions about.

Question 6

what is a sample

Answer 6

A sample is a group of units selected from a larger group (the population) for the study.

Question 7

why we select samples for data analysis

Answer 7

• A sample is generally selected for study because the population is too large to study in its entirety. The sample should be representative of the general population. This is often best achieved by random sampling.

Question 8

what is descriptive statistics

Answer 8

brief descriptive coefficients that summarize a given data set, which can be either a representation of the entire population or a sample of a population.

Question 9

why descriptive statistics are important

Answer 9

we get acquainted with the data, calculate the outliers, asses assumptions needed to check statistical hypothesis, check that the data doesnt have any errors

Question 10

describe nominal (categorical/qualitative) variable

Answer 10

values are in arbitrary (sutartinis)categories and no units

Question 11

describe ordinal (categorical/qualitative) variable

Answer 11

values in ordered categories and no units

Question 12

describe discrete (metric/quantitative) variable

Answer 12

integer (sveiki skaiciai), counted units

Question 13

describe continuous (metric/quantitative) variable

Answer 13

continuous values, measured units

Question 14

how can we systemise descriptive statistics

Answer 14

frequency and contingency tables, charts

Question 15

how to summarise metric data

Answer 15

Mode, median, mean, quartile, variance, standard deviation

Asymmetry, kurtosis , Charts , histogram

Question 16

what is a limitation of histograms

Answer 16

they can only represent one variable

Question 17

how to write assumption of normality?

Answer 17

mean ± SD

Question 18

what is mean

Answer 18

The mean is the arithmetic average of the observations

Question 19

what is variance

Answer 19

the spread around the mean.

Question 20

standard deviation

Answer 20

a quantity expressing by how much the members of a group differ from the mean value for the group.

Question 21

why analysing only the mean is not accurate

Answer 21

• Analysing just the mean values is not as accurate, due to various values that could be present in the data set. Therefore we need to look at the variance of the data and SD to evaluate the accuracy.

Question 22

what the empirical rule states?

Answer 22

The empirical rule states that for a normal distribution (bell shaped histogram), nearly all of the data will fall within three standard deviations of the mean.

Question 23

the empirical rule

Answer 23

In particular, the empirical rule predicts that 68% of observations falls within the first standard deviation (µ ± σ), 95% within the first two standard deviations (µ ± 2σ), and 99.7% within the first three standard deviations (µ ± 3σ)

Question 24

standard error definition

Answer 24

• The SD of the sample means, known as the standard error (SE), measures the deviation of individual sample means from the population mean. It measures the variability in the sample means.

Question 25

what small and large SE values indicate

Answer 25

small - little variation between the sample means

large - much variation in the sample means

Question 26

what determines the size of SE?

Answer 26

The size of the SE depends on the variation between individuals in the population and on the sample size, and is calculated as follows: SE = s/sqrt(n), where s is the SD of the sample and n is the sample size

Question 27

why using a large sample size is beneficial?

Answer 27

Larger sample sizes lead to smaller standard errors (the denominator, n, is larger). Large sample sizes produce more precise estimates of the population value in question.

Question 28

does 95% means probability?

Answer 28

no

Question 29

what 95% means

Answer 29

95% mean that 5% of intervals will not include the population mean

Question 30

why SE is useful

Answer 30

Standard error is a useful measure of uncertainty of the mean. However, it is better to use confidence intervals instead

Question 31

define the median

Answer 31

The median is the middle observation, that is, the point at which half the observations are smaller and half are larger. Symbolized by Md or M.

Question 32

is median sensitive to extreme values?

Answer 32

no

Question 33

symmetric histogram

Answer 33

If the mean and the median are equal, the distribution of observations is symmetric.

Question 34

skewed to the right

Answer 34

If the mean is larger than the median, the distribution is skewed to the right.

Question 35

skewed to the left

Answer 35

If the mean is smaller than the median, the distribution is skewed to the left.

Question 36

define the quartiles

Answer 36

• The quartiles of a ranked set of data values are the three points that divide the data set into four equal groups, each group comprising a quarter of the data.