Hypothesis testing

Question 1

What is the Domain/ Study sample/

Answer 1

Population in which the question is answered

Question 2

What is the exposure/ determinant?

Answer 2

The causes and other factors that influence the occurence of disease and other health-related events

Question 3

What is the ‘Outcome’?

Answer 3

The endpoint of interest

Question 4

What is the definition of Data?

Answer 4

Information, such as facts or numbers collected together to be examined

Question 5

What is the definition of Variables?

Answer 5

The attributes that change among the variables

Question 6

What are nominal characteristics?

Answer 6

characteristics with no ordering of the categories

Question 7

What are ordinal characteristics?

Answer 7

Characteristics with clear ordering
e.g they have scores of relative abundance

Question 8

What is discrete data?

Answer 8

Data that only takes integer values
e.g number of leaves

Question 9

What is continuous data?

Answer 9

Data that can have almost any numeric value and is then divided into finer and finer levels

Question 10

What is descriptive statistics?

Answer 10

summarising data by using tables, diagrams and summary measures

Question 11

What are inferential statistics?

Answer 11

trying to reach conclusions that apply to the entire population based on some data from a sample

Question 12

What is a confidence interval?

Answer 12

an estimated interval within which an unknown parameter may plausibly lie

specifically- A 95%CI is the result of a procedure that, in 95% of cases in which its assumption are correct, will contain the true parameter value.

Question 13

What is a null hypothesis?

Answer 13

Where there is no significant difference in the populations under investigation

Question 14

What is hypothesis testing?

Answer 14

The formal procedure to accept or reject statistical hypotheses

Question 15

What is the alternative hypothesis?

Answer 15

denoted by H1 and is contrary to the null hypothesis

Question 16

What is the p-value?

Answer 16

The probability of getting the observed effect if the null hypothesis is true

Question 17

What is the effect of a small p value?

Answer 17

The smaller the p value the stronger the evidence against the null hypothesis

Question 18

What is a t-test for?

Answer 18

Comparing the means of two groups
provided that the two samples are independent, the variables
normally distributed and they must have approximately the same
variances

Question 19

What is an ANOVA test for?

Answer 19

Comparing the means of more than two groups
*provided that the samples are independent and the variables are
normally distributed. *

Question 19

What is a chi-squared test for?

Answer 19

Comparing two proportions
provided a large sample size, each individual is represented once..

Question 19

What is a power analysis?

Answer 19

a calculation that helps you determine a minimum sample size for your study

Question 20

What does statistics involve, broadly

Answer 20

• Collection and analysis of data
• summarising information in order to aid understanding
• interpretation of analyses and drawing conclusions from the data

Question 21

What are the two different types of data?

basic classification

Answer 21

• Categorical/ Qualitative
• Numerical/ Quantitative

Question 22

How would you define Categorical/ qualitiative data?

Answer 22

They describe a characteristic that cant be easily measured but can be observed subjectively

Question 23

How would you describe numerical or quantitative data?

Answer 23

They describe a measurable quality on a well-defined scale

Question 24

What are some different ways you can calculate confidence intervals?

Answer 24

Depends on what you are measuring * means * proportions * difference in mean * Relative risk * odds ratio * regression coefficients under general assumption the data comes from normal distribution

Question 25

What does the p=value summarise?

Answer 25

The p value summarises the strength of the evdience going against the null hypothesis

Question 26

What are six principles that address the msiconceptions and missuse of the p= value?

Answer 26

1. p-values can indicate how incompatible the data are with a specified statistical model. 2. p-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone. 3. Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold. 4. Proper inference requires full reporting and transparency. 5. A p-value, or statistical significance, does not measure the size of an effect or the importance of a result. 6. By itself, a p-value does not provide a good measure of evidence regarding a model or hypothesis.

Question 27

Why is power analysis important?

Answer 27

If the study does not have enough 'power' then your study may not be able to detect a true 'effect' making it a waste of time, money and unethical

Question 28

What is a type I error?

Answer 28

A false positive so rejecting the null hypothesis (positive result) incorrectly

Question 29

What is a type II error?

Answer 29

A false negative so incorrectly retaining a false null hypothesis