BIO 243-Module 1

Five hierarchical scales:

 Population of interest > Statistical population > Sample > Sampling unit > Observation unit

Sampling unit

The unit being selected at random

For example, if you randomly selected 100 email addresses to gather data on grocery store preference, then the sampling unit is the email address of a person. The sampling unit may be the same as the observation unit, or it may contain multiple observation units.

Sample

The collection of sampling units that you randomly selected.

For example, if 72 people replied to your email about their favourite grocery store, then your sample includes the 72 email responses

Observation unit

The scale for data collection. You can think of this as the subject of the study. For example, if we were to ask people which grocery store was their favourite, the observation unit would simply be the individual person

Statistical population

The collection of all sampling units that could have been in your sample, and represents the true scale in which your statistical conclusions are valid.

For example, let's say that you decided to collect your data by sending an email out to a 100 random people from a list of all emails in the City of Toronto. The statistical population would then be all people in Toronto with an active email account.

Population of interest

The collection of sampling units that you hope to draw a conclusion about. In contrast to the statistical population, which is defined by the technical details of your sampling design, the population of interest is defined by the scope of your research question.

For example, if your research question is about the proportion of people who shop at large grocery stores as opposed to locally owned corner stores in Toronto, then the population of interest is all the people in Toronto. Ideally the population of interest is the same as your statistical population, but often the population of interest is larger.

Measurement variable

What we want to measure about the observation unit, such as height, age or voting intent

Measurement unit

The scale of the measurement variable, such as centimetres for height or years for age. If the data are categorical, such as voting intent, then there is no measurement unit.

EX: You want to find the average weight of the cows you have at your farm, so you take the sample of 10 cows. What are the five hierarchical scales, as well as the measurement variable and unit.

Population of interest: All cattle on your farm

Statistical population: All cattle on your farm

Sampling unit: A cow

Sample: The 10 cows you randomly selected

Observation unit: A cow

Measurement variable: Weight

Measurement unit: Kg

EX:You want to find the average income in a neighbourhood, so you take the sample of 22 house in the neighbourhood. What is the sampling unit and the statistical population.

What is the sampling unit: A house

What is the Statistical Population: All the houses in the neighbourhood

Descriptive statistics

Characterize the data in your sample and includes things like averages, tables, and graphs

Inferential statistics

Uses information from your sample to make a probabilistic statement about the statistical population.

The 4 steps in the frame work:

Sampling, measuring, calculating descriptive statistics, calculating inferential statistics

Sampling

The step of creating your study design and collecting your samples. This includes taking a sample then sampling unit and then observation unit from your Statistical population.

Measuring

The step of taking measurements from your observation units, which gives you the data with which to work. It may be just a single measurement variable from the observation unit (e.g., weight of a cow) or multiple measurement variables (e.g., weight, age, and health of a cow).

Calculating descriptive statistics

The step where you describe the data in your sample. This may include calculating the average value of a measurement variable in your data set, calculating the variation among measurements, or creating graphs.

Calculating inferential statistics

The final step where you use the information contained in your data to draw a conclusion about the statistical population

Single versus multiple groups

When there are multiple groups within a statistical population, descriptive statistics are used to characterize the sample data for each group. Inferential statistics are used to make broader statements about each group in the context of the statistical population, and to make statements about the differences among the groups.

EX: Complete the following sentence: "Inferential statistics...”

…Allow you to make probabilistic statements about statistical population

EX: Complete the following sentence: "Descriptive statistics..."

…Characterize some aspect of the data in your sample

…Can be as simple as calculation an average value