Give the meaning for the five terms MONASH University sct10 20: Introduction to

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Give the meaning for the five terms MONASH University sct10 20: Introduction to Statistical Reasoning WEEK 5: INTRODUCTION to SAMPLING and EXPERIMENTAL DESIGN-How data are collected. Tutorial DayTime D S Moore et al,"Basic Practice ef Statistics", Chs 8&9 On completion of this workshop you should be abile to I. Select an appropriane design for a given esample of a stady Identity the principkes of statistical design of experiments: contnol randomisation and replication 4 Memity these Seaares in a randomized comparative esperimen by consacting a schematik chat or the desigs, S. Identify aconfuning vanable and how itincorpotaled mo a tandetaned block cspennatal dagn, 6 Idemity a matched pair espenimental design Distingu betwom difforent sampling mehods and idenity the mostappropriatie mpling method for the These problems are to help you engage with the lecture material, and also to make sure that everyone is up-to-speed before the workshop starts, Please make sure you do them brefore class each week 01 State in your own words what is meant by each of the terms listed below. Term Sample Population (Simple) random Systematic sample Stratified random Weck 5 Copyright 2017: Monash University Pape 11

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Sample: A sample is a smaller, manageable version of a larger group. It is a subset containing the characteristics of a larger population. Samples are used in statistical testing when population sizes are too large for the test to include all possible members or observations.

Population: In statistics, population refers to the total set of observations that can be made.

For example, if we are studying the weight of adult women, the population is the set of weights of all the women in the world. If we are studying the grade point average (GPA) of students at Harvard, the population is the set of GPA's of all the students at Harvard.

In statistics, the term population is used to describe the subjects of a particular study—everything or everyone who is the subject of a statistical observation. Populations can be large or small in size and defined by any number of characteristics, though these groups typically defined specifically rather than vaguely—for instance, a population of women over 18 who buy coffee at Starbucks rather than a population of women over 18.

Simple Random sample: A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen. An example of asimple random sample would be the names of 25 employees being chosen out of a hat from a company of 250 employees.

Systematic sample: Systematic sampling is a type of probabilitysampling method in which sample members from a larger population are selected according to a randomstarting point and a fixed periodic interval. This interval, called the sampling interval, is calculated by dividing the population size by the desired sample size.

Stratified random sample: Stratified random sampling is a method of sampling that involves the division of a population into smaller groups known as strata. In stratified random sampling, the strata are formed based on members' shared attributes or characteristics. ... These subsets of the strata are then pooled to form a random sample.

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