Basic Statistics Definitions
29 Questions
0 Views

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

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 nN.
  • 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

Description

Test your knowledge of key statistical terms and concepts. This quiz covers definitions such as population, sample, descriptive and inferential statistics, and more. Perfect for students looking to strengthen their understanding of basic statistics.

More Like This

Statistics Definitions and Concepts
16 questions
Definición y Objetivos de la Estadística
21 questions
Basic Statistics Definitions
29 questions

Basic Statistics Definitions

TroubleFreeGyrolite7426 avatar
TroubleFreeGyrolite7426
Use Quizgecko on...
Browser
Browser