Social Data Analytics - Lecture 2.1
21 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 is the main difference between descriptive and inferential statistics?

  • Descriptive statistics use probabilistic techniques to analyze a sample, while inferential statistics describe and summarise data.
  • Descriptive statistics use probabilistic techniques, while inferential statistics do not.
  • Inferential statistics aim to make conclusions about a wider population based on sample analysis, while descriptive statistics describe and summarise data. (correct)
  • Inferential statistics describe and summarise data, while descriptive statistics aim to make conclusions about a wider population.

    • What is the difference between a statistic and a parameter?

  • A parameter is a descriptive measure, while a statistic is an inferential measure.
  • A statistic is calculated from a sample, while a parameter is calculated from the entire population. (correct)
  • A parameter is a characteristic of a sample, while a statistic is a characteristic of the population.
  • A statistic is a descriptive measure, while a parameter is an inferential measure.

    • What does the 'CLT' stand for in the context of the given lecture?

  • Central Line Theorem
  • Central Limit Theorem (correct)
  • Cumulative Limit Theorem
  • Common Law Theory

    • What is a census?

    <p>A complete enumeration of a population, including every member. (D)</p> Signup and view all the answers

    Which of the following is NOT a branch of inferential statistics?

    <p>Sampling Distributions (D)</p> Signup and view all the answers

    Which of the following is a characteristic of a population?

    <p>Population proportion. (A)</p> Signup and view all the answers

    Which of the following is a characteristic of a sample?

    <p>The average income of a random sample of 1000 adult males in the UK. (A)</p> Signup and view all the answers

    Which of the following is NOT mentioned as an application of inferential statistics in the provided lecture content?

    <p>Sampling Distributions (D)</p> Signup and view all the answers

    Which of the following sampling methods is NOT mentioned as a popular probability sampling design?

    <p>Quota sampling (A)</p> Signup and view all the answers

    What is the main issue with making inferences about a population based on a non-random sample?

    <p>The sample may not be representative of the population. (D)</p> Signup and view all the answers

    What does the provided content suggest about the relationship between sample size and the variability of the sampling distribution?

    <p>Smaller samples lead to greater variability. (C)</p> Signup and view all the answers

    The provided content mentions a specific example of a situation where inferences about the population were incorrect due to a non-random sample. What was this example?

    <p>Opinion polls conducted prior to the 2015 general election. (D)</p> Signup and view all the answers

    According to the recommended reading suggestion, what is the primary focus of Chapter 8 in 'Beginning Statistics' by Foster, Diamond, and Jefferies?

    <p>The Central Limit Theorem. (B)</p> Signup and view all the answers

    What is a primary reason for using a sample instead of collecting data from the entire population?

    <p>Samples are less expensive and time-consuming to collect data from compared to populations. (D)</p> Signup and view all the answers

    Which of the following is NOT a source of non-sampling error?

    <p>Random selection of participants in a sample. (D)</p> Signup and view all the answers

    Which type of sampling method guarantees that each member of the population has an equal chance of being selected for the sample?

    <p>Simple random sampling. (C)</p> Signup and view all the answers

    In the context of statistical inference, what is meant by "population parameter"?

    <p>A characteristic of the entire population, such as the average income. (B)</p> Signup and view all the answers

    Explain the concept of "sampling error" in the context of using a sample to represent a population.

    <p>Sampling error is the difference between the true population parameter and the sample statistic. (B)</p> Signup and view all the answers

    Why is it important to use a probability sampling method, such as simple random sampling, when conducting statistical inference?

    <p>Probability sampling methods help to ensure that the sample is representative of the population. (C)</p> Signup and view all the answers

    Imagine a researcher wants to estimate the average age of students in a university. They decide to collect data from a sample of 100 students. Which of the following is an example of non-sampling error in this study?

    <p>The researcher mistakenly records the age of a student as 20 years old instead of 21 years old. (A)</p> Signup and view all the answers

    Which of the following is a key assumption underlying the methods used in this module for statistical inference?

    <p>The sample is selected using simple random sampling. (D)</p> Signup and view all the answers

    Study Notes

    Social Data Analytics - Lecture 2.1: From Sample to Population

    • Statistics has two main branches: descriptive and inferential
    • Descriptive statistics describes, organises, summarises, and displays data
    • Inferential statistics uses probabilistic techniques to analyse a sample to understand the population
    • A population is all members of a group (e.g., all adults in the UK)
    • A census is a complete count of a population
    • A sample is a subset of a population
    • A statistic describes a characteristic of a sample (e.g., the mean weight of people in a sample)
    • A parameter describes a characteristic of a population (e.g., the proportion voting Conservative)
    • An example: To find the 2014 UK poverty rate, the UK Family Resources Survey sampled 23,000 households
    • All survey estimates use a sample
    • Samples are used to draw inferences about a population
    • A sampling error occurs when a sample is used instead of the entire population, resulting in variation
    • A non-sampling error is a mistake in the sampling process (i.e., poor questions, bias by interviewers, errors in measurements)
    • To infer something about the population, random sampling is essential, along with various inference tools
    • Probability sampling ensures each population member has a known non-zero chance of selection
    • Simple random sampling (SRS) ensures equal chance of selection
    • Other probability sampling designs include stratified simple random sampling, cluster sampling, systematic sampling, and multi-stage sampling
    • If a sample is not random, inferences about the population are unreliable
    • Sampling distributions are used to generalise from small samples to a population of potentially millions
    • A sampling distribution represents the possible values of a statistic (like a sample mean) over repeated samples from a population, allowing inference
    • The Central Limit Theorem (CLT) states that the sampling distribution of the sample means will be approximately normal, even if the population is not normal
    • The mean of the sampling distribution is approximately equal to the population mean (µ), regardless of the population distribution shape
    • This only applies for large random samples (typically 30 or more)
    • Standard errors (SE) measure the variability of a sampling distribution, useful for inferring the population value. The formula depends on whether you're finding the SE for a mean or a proportion.
    • The standard error for the sample mean is the population standard deviation (σ) divided by the square root of the sample size (n)
    • The standard error for the sample proportion is the square root of (π(1- π)/n), where π is the population proportion.
    • The sample standard deviation (s) is used when the population standard deviation isn't known. The sample proportion (p) is used when the population proportion isn't known.
    • Larger sample sizes yield smaller SEs, thus providing more information about the population
    • Key reading: Chapter 8 of "Beginning Statistics" by Foster, Diamond, and Jefferies (2015)
    • Check the pre-recorded lecture for CLT demonstrations

    Studying That Suits You

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

    Quiz Team

    Related Documents

    RESM2001 (2024-25) Lecture 2.1 - From Sample To Population PDF

    Description

    Explore the fundamentals of statistics in this lecture on Social Data Analytics. Discover the difference between descriptive and inferential statistics, the role of samples and populations, and how sampling errors can affect analysis. This quiz will deepen your understanding of key statistical concepts used in social research.

    More Like This

    Use Quizgecko on...
    Browser
    Open
    Browser
    Browser