Basic Statistics Definitions

Choose a study mode

Play Quiz
Study Flashcards
Spaced Repetition
Chat to Lesson

Podcast

Play an AI-generated podcast conversation about this lesson

Questions and Answers

What were the two main factors that the experimental units were exposed to in this study?

The two main factors were daily caloric intake and daily exercise level.

List the levels of daily caloric intake set up in the experiment.

The levels were 1500 – 2000 calories and 2000 – 2500 calories.

How many treatment groups were formed in the experiment, and what was the sample size for each group?

There were 6 treatment groups, with a sample size of 80 men in each group.

What potential bias was identified in this experiment regarding exercise levels?

<p>The potential bias was that participants might assume no exercise would lead to less weight loss, which could affect their motivation.</p> Signup and view all the answers

Explain the significance of random assignment in this study.

<p>Random assignment helps minimize selection bias, ensuring that each treatment group is comparable.</p> Signup and view all the answers

What is the primary difference between a sample and a population?

<p>A sample is a selection or subset of members from a population, while a population is the entire group to be studied.</p> Signup and view all the answers

What is the characteristic of Simple Random Sampling?

<p>Every element of the population has an equal likelihood of being chosen.</p> Signup and view all the answers

Explain the two main branches of statistics.

<p>Descriptive statistics involves organizing and summarizing information, while inferential statistics involves drawing conclusions about a population based on sample data.</p> Signup and view all the answers

How does Stratified Random Sampling differ from Simple Random Sampling?

<p>Stratified Random Sampling involves selecting elements randomly from specific subgroups within the population.</p> Signup and view all the answers

What is the process of Cluster Sampling?

<p>Cluster Sampling divides the population into clusters and randomly samples entire clusters.</p> Signup and view all the answers

Define univariate data and provide an example.

<p>Univariate data involves a single variable sampled on elements; for example, measuring the height of students in a class.</p> Signup and view all the answers

What distinguishes qualitative variables from quantitative variables?

<p>Qualitative variables measure non-numerical characteristics, while quantitative variables measure characteristics that can be ranked or ordered on a numerical scale.</p> Signup and view all the answers

Describe Systematic Sampling and its selection method.

<p>Systematic Sampling involves selecting every mth element from a randomly chosen starting point.</p> Signup and view all the answers

What is Convenience Sampling and where is it commonly applied?

<p>Convenience Sampling involves selecting elements that are easily accessible.</p> Signup and view all the answers

Describe discrete quantitative variables and give an example.

<p>Discrete quantitative variables can only take on certain values, typically integers; for example, the number of students in a classroom.</p> Signup and view all the answers

Explain Judgement Sampling and its purpose.

<p>Judgement Sampling is where the experimenter selectively chooses participants based on specific criteria.</p> Signup and view all the answers

What is the purpose of a sampling design in research?

<p>A sampling design is a methodology for choosing elements from a population to form a sample, ensuring valid data collection.</p> Signup and view all the answers

Differentiate cross-sectional data from time-series data.

<p>Cross-sectional data is sampled at a particular point in time, while time-series data is sampled at multiple points in time for the same elements.</p> Signup and view all the answers

What distinguishes Observational Studies from Designed Experiments?

<p>Observational Studies involve merely observing characteristics without intervention, while Designed Experiments include treatment applications.</p> Signup and view all the answers

What is the difference between a census and a sampling survey?

<p>A census collects data from all members of a population, whereas a sampling survey collects data from a subset of the population.</p> Signup and view all the answers

Why is randomness important in sampling designs?

<p>Randomness improves the ability to make accurate projections and inferences about the population based on the sample.</p> Signup and view all the answers

What is the purpose of a control group in an experiment?

<p>The control group provides a baseline for comparison, allowing researchers to determine the effect of the treatment.</p> Signup and view all the answers

Define randomization in the context of experimental design.

<p>Randomization is the process of randomly assigning subjects to groups to minimize selection bias and ensure comparable groups.</p> Signup and view all the answers

What are the three key principles of experimental design?

<p>The three key principles are control, randomization, and replication.</p> Signup and view all the answers

Explain what a completely randomized design involves.

<p>In a completely randomized design, all experimental units are assigned randomly to all treatment groups without any restrictions.</p> Signup and view all the answers

What distinguishes a double-blind experiment from a blind experiment?

<p>In a double-blind experiment, neither the participants nor the experimenters know which treatment is being administered.</p> Signup and view all the answers

How does replication contribute to an experimental study?

<p>Replication involves using a sufficient number of subjects to ensure reliable results and enhances the likelihood of detecting treatment differences.</p> Signup and view all the answers

What role do levels play in experimental factors?

<p>Levels represent the possible values or categories of a factor that are tested in an experiment.</p> Signup and view all the answers

Describe the importance of sampling from similar backgrounds in an experimental study.

<p>Sampling from similar backgrounds helps control for confounding variables, ensuring that the results are due to the treatment effect.</p> Signup and view all the answers

Flashcards

Simple Random Sampling

Every element in the population has an equal chance of being selected.

Stratified Random Sampling

Dividing the population into subgroups and randomly selecting elements from each subgroup.

Cluster Sampling

Dividing the population into clusters and randomly selecting clusters to include all elements in those clusters.

Population

The entire group of individuals or objects that are being studied.

Signup and view all the flashcards

Sample

A smaller, representative group selected from the population for analysis.

Signup and view all the flashcards

Systematic Sampling

Selecting the kth element from the first m elements randomly, then selecting every mth element following it.

Signup and view all the flashcards

Convenience Sampling

Sampling individuals who are easily accessible.

Signup and view all the flashcards

Descriptive Statistics

Methods for organizing and summarizing data.

Signup and view all the flashcards

Inferential Statistics

Methods for drawing conclusions about a population based on a sample.

Signup and view all the flashcards

Judgement Sampling

Sampling individuals based on the researcher's judgment.

Signup and view all the flashcards

Controlled Experiment

A research study design where all variables except for the specific factor(s) being investigated are kept constant.

Signup and view all the flashcards

Observational Study

Researchers observe characteristics and take measurements without manipulating any variables.

Signup and view all the flashcards

Element

A single member of a population or sample.

Signup and view all the flashcards

Treatment Factors

Factors deliberately manipulated in an experiment to observe their effects on the outcome.

Signup and view all the flashcards

Designed Experiment

Researchers manipulate variables and observe the effects on measurements.

Signup and view all the flashcards

Treatments

Different combinations of treatment factor levels that are used in an experiment.

Signup and view all the flashcards

Sampling

The process of choosing elements from a population to form a sample.

Signup and view all the flashcards

Variable

A characteristic of the members of a population that can be measured.

Signup and view all the flashcards

Control Group

A group in an experiment that does not receive the treatment being studied, serving as a baseline for comparison.

Signup and view all the flashcards

Qualitative Variable

A variable that measures a non-numerical characteristic.

Signup and view all the flashcards

Bias from Lack of Blinding

A potential source of error in a study where the participants' knowledge of the treatment may influence their responses.

Signup and view all the flashcards

Response Variable

The variable being measured in an experiment to see the effect of different treatments.

Signup and view all the flashcards

Factor

A variable that you manipulate to see its impact on the response variable.

Signup and view all the flashcards

Levels

The specific values or categories of a factor. Example: "Low, Medium, High" for the factor "Dosage".

Signup and view all the flashcards

Treatment Group

A group of subjects receiving a particular treatment.

Signup and view all the flashcards

Completely Randomized Design

All participants are assigned randomly to each treatment group. Ensures each group is as similar as possible.

Signup and view all the flashcards

Randomized Block Design

Participants are grouped based on a characteristic (like age or weight), then assigned randomly to treatments within each group.

Signup and view all the flashcards

Study Notes

Basic Statistics Definitions

  • Population: The entire group being studied. Represented by N, the size of the population.
  • Sample: A subset of the population, used for analysis and projection back to the population. Represented by n, where n ≤ N.
  • Descriptive Statistics: Mathematical methods for organizing and summarizing information.
  • Inferential Statistics: Methods to draw conclusions about a population based on a sample, assessing the reliability of those conclusions.

Key Concepts

  • Element/Experimental Unit: A single member of a population or sample.
  • Sampling: The process of selecting elements from a population to create a sample, often with a specific methodology (Sampling Design).
  • Variable: A characteristic of the population members that can be measured.
    • Qualitative Variable: Characterizes non-numerical features.
    • Quantitative Variable: Measures characteristics using numerical scales.
      • Discrete Quantitative Variable: Can only take specific values, usually integers.
      • Continuous Quantitative Variable: Can take on any value within a given range.

Sampling Designs

  • Simple Random Sampling: Every population member has an equal chance of selection. Uses random number generators/tables.
  • Stratified Random Sampling: Random selection from subgroups (strata) within the population, with the proportion of elements from each stratum proportional to their representation in the whole population. Can choose specific numbers from each stratum to represent demographics (quota sampling).
  • Cluster Sampling: Dividing the population into naturally occurring clusters and then randomly choosing clusters (one-stage) or selecting sub-samples from chosen cluster (two-stage).
  • Systematic Sampling (1-in-m sampling): Choosing a random starting point and selecting every mth element thereafter.

Data Types

  • Univariate Data: Data describing a single characteristic of items.
  • Bivariate Data: Data about two characteristics.
  • Multivariate Data: Data involving three or more characteristics.
  • Cross-Sectional Data: Data collected at a single point in time across a population.
  • Time Series Data: Data collected at multiple points in time for a population.
  • Census: Data collected from all population members.
  • Sampling Survey: Data collected from a sample subset of a population.

Experimental Designs

  • Observational Studies: Researchers observe and measure characteristics in a sample.
  • Designed Experiments: Researchers apply treatments and controls to observe the effect on a variable
    • Treatment: An experimental condition
    • Response Variable: A variable measuring the experimental effect
    • Factor: A variable of interest influencing the effect
    • Levels: Possible values of a factor
    • Treatment Group: A group experiencing a treatment (or more).
    • Control Group: A similar group with no treatment or baseline treatment.

Principles of Experimental Design

  • Control: Reduces effects of confounding factors (not the one of interest)
  • Randomization: Randomly assigns elements to groups to minimize bias.
  • Replication: Uses a sufficient number of participants for the randomization to produce similar groups.

Specific Design Examples

  • Completely Randomized Design: All experimental units are randomly assigned to treatments.
  • Randomized Block Design: Experimental units are assigned randomly to treatments in blocks (e.g., by similar characteristics).
  • Blind Experiment: Participants are unaware of their assigned treatment (minimizes bias).
  • Double-Blind Experiment: Neither participants nor experimenters know assignments (minimizing bias).

Studying That Suits You

Use AI to generate personalized quizzes and flashcards to suit your learning preferences.

Quiz Team

Related Documents

Chapter 1 Definitions PDF

More Like This

Use Quizgecko on...
Browser
Open
Browser
Browser