1.2 Data, Sampling, and Variation in Data and Sampling
4 min read•june 27, 2024
Data types and sampling methods are crucial in statistics. describes attributes, while uses numbers. Understanding these helps in choosing the right analysis techniques for different kinds of information.
Random sampling ensures unbiased representation of a . Simple random, stratified, cluster, and are common methods. Each has its strengths, helping researchers gather accurate data for various study designs and population types.
Types of Data and Sampling Methods
Qualitative vs quantitative data
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- Qualitative data consists of non-numeric attributes, characteristics, or categories that describe the data
- Cannot be measured or counted numerically
- Analyzed using frequencies, proportions, or percentages
- Examples: hair color (blonde, brown, black, red), movie genres (action, comedy, drama, horror), phone brands (Apple, Samsung, Google, OnePlus)
- Quantitative data is numeric and can be measured, counted, or expressed using numbers
- quantitative data has countable values, often integers
- Represents a fixed number of possible values
- Examples: number of pets owned (0, 1, 2, 3), number of languages spoken (1, 2, 3, 4), number of bedrooms in a house (1, 2, 3, 4, 5)
- quantitative data is measurable and can take on any value within a range
- Represents an infinite number of possible values
- Examples: body temperature (98.6°F, 99.2°F, 100.5°F), distance traveled (5.2 miles, 10.7 miles, 26.4 miles), time spent studying (1.5 hours, 2.75 hours, 4.33 hours)
- quantitative data has countable values, often integers
Interpretation of two-way tables
- , also known as , display frequencies or counts of data based on two categorical variables
- Rows represent levels of one variable, while columns represent levels of the other variable
- Each cell shows the frequency or count for a specific combination of the two variables
- provide information about a single variable, ignoring the other variable
- Found by summing the frequencies or counts across each row or column
- Row totals and column totals represent the marginal distributions
- is calculated as:
- show the frequencies or proportions of one variable, given a specific level of the other variable
- Calculated by dividing a cell value by its corresponding row or column total
- is calculated as:
- , read as "probability of A given B"
- , read as "probability of B given A"
- Interpretation: "Given that event B has occurred, what is the probability of event A occurring?" or vice versa
Methods of random sampling
- ensures that each member of the population has an equal chance of being selected
- Randomly select a of size n from a population of size N
- Provides an unbiased and representative sample
- Example: randomly selecting 100 students from a school of 1,000 students using a random number generator
- involves dividing the population into homogeneous subgroups () based on a specific characteristic
- Simple random sampling is then performed within each stratum
- Ensures representation of all subgroups in the sample
- Example: dividing a city's population by income levels (low, medium, high) and randomly sampling from each income level
- divides the population into naturally occurring groups (clusters)
- Randomly select a sample of clusters, and include all members within the selected clusters
- Useful when a complete list of population members is not available
- Example: randomly selecting 10 city blocks and surveying all households within those blocks
- Systematic sampling starts by randomly selecting a starting point from the population
- Choose every kth element from the list, where k = N/n (population size divided by sample size)
- Ensures even distribution of the sample across the population
- Potential for if there is a periodic pattern in the population list
- Example: selecting every 10th person from a list of 1,000 people, starting at a randomly chosen point
Measures of Variability and Statistical Inference
- Standard deviation measures the average distance between each data point and the mean
- Provides insight into the spread or dispersion of the data
- Calculated as the square root of the
- Variance quantifies the average squared deviation from the mean
- Useful for comparing the spread of different datasets
- Larger variance indicates greater variability in the data
- states that the of the sample mean approaches a normal distribution as the sample size increases
- Applies regardless of the shape of the population distribution
- Enables the use of normal distribution properties for statistical inference
- Sampling distribution represents the distribution of a statistic (e.g., sample mean) for all possible samples of a given size from a population
- Provides information about the variability of the statistic across different samples
- is a range of values that likely contains the true population parameter
- Based on the sample statistic and its standard error
- Wider intervals indicate less precision in the estimate
Key Terms to Review (22)
Central Limit Theorem: The central limit theorem states that the sampling distribution of the sample mean will be approximately normal, regardless of the shape of the population distribution, as the sample size increases. This theorem is a fundamental concept in statistics that underpins many statistical inferences and analyses.
Cluster Sampling: Cluster sampling is a type of probability sampling method where the population is divided into distinct groups or clusters, and then a random sample of those clusters is selected for data collection. The selected clusters are then used to represent the entire population.
Conditional Distributions: Conditional distributions describe the distribution of a random variable given that another random variable has a specific value. They are a fundamental concept in probability and statistics, used to understand the relationship between variables and make inferences about one variable based on the value of another.
Conditional Probability: Conditional probability is the likelihood of an event occurring given that another event has already occurred. It represents the probability of one event happening, given the knowledge or occurrence of another related event.
Confidence Interval: A confidence interval is a range of values that is likely to contain an unknown population parameter, such as a mean or proportion, with a specified level of confidence. It provides a way to quantify the uncertainty associated with estimating a population characteristic from a sample.
Contingency Tables: A contingency table, also known as a cross-tabulation or two-way table, is a statistical tool used to display and analyze the relationship between two or more categorical variables. It provides a way to investigate the association or dependence between these variables by organizing the data into a tabular format.
Continuous: Continuous refers to a characteristic of data or a variable that can take on any value within a given range, rather than being limited to a set of discrete or distinct values. It is a fundamental concept in the understanding of data, sampling, and variation.
Discrete: Discrete refers to data or variables that can only take on specific, distinct values, rather than a continuous range of values. It is a fundamental concept in the context of data, sampling, and variation in data and sampling.
Marginal Distributions: Marginal distributions are the individual probability distributions of each variable in a multivariate probability distribution. They represent the distribution of a single variable, independent of the other variables in the dataset.
Marginal Probability: Marginal probability refers to the likelihood or probability of an event occurring independently, without considering the relationship or interaction with other events. It represents the overall or unconditional probability of a single event happening, regardless of the occurrence of other events.
Population: In the context of statistics, a population refers to the entire set of individuals, objects, or measurements of interest that a researcher wants to study or draw conclusions about. It represents the complete group that is the focus of the statistical analysis, from which a sample may be drawn for further investigation.
Qualitative Data: Qualitative data is information that cannot be measured numerically, but rather is described in words, observations, and other non-numerical forms. It provides insights into concepts, opinions, and experiences that cannot be easily quantified.
Quantitative Data: Quantitative data refers to numerical information that can be measured, counted, or expressed using numbers. It is a type of data that provides precise and objective measurements, allowing for statistical analysis and mathematical calculations.
Sample: A sample is a subset of a larger population that is selected to represent the characteristics of the entire population. It is a crucial concept in statistics, probability, and data analysis, as it allows researchers to draw inferences about the population based on the information gathered from the sample.
Sampling Bias: Sampling bias occurs when a sample is not representative of the population being studied, leading to distorted or inaccurate conclusions. It arises from the way the sample is selected, resulting in systematic errors that skew the data and prevent it from accurately reflecting the true characteristics of the population.
Sampling Distribution: The sampling distribution is a probability distribution that describes the possible values a statistic, such as the sample mean or sample proportion, can take on when the statistic is calculated from random samples drawn from a population. It is a fundamental concept in statistical inference and is crucial for understanding the behavior of sample statistics and making inferences about population parameters.
Simple Random Sampling: Simple random sampling is a method of selecting a sample from a population where each individual has an equal probability of being chosen. This ensures that the sample is representative of the larger population, allowing for unbiased statistical inferences to be made.
Strata: Strata refers to the distinct layers or subgroups within a population that are used in sampling and data collection. These layers or subgroups are typically defined by characteristics that are relevant to the research question or analysis being conducted.
Stratified Sampling: Stratified sampling is a probability sampling technique in which the population is divided into distinct subgroups or strata, and a random sample is then selected from each stratum. This method ensures that the sample is representative of the overall population by capturing the diversity within the different strata.
Systematic Sampling: Systematic sampling is a type of probability sampling method where elements are selected from a population at a regular, predetermined interval. This approach ensures a more representative sample is drawn from the target population compared to simple random sampling.
Two-Way Tables: A two-way table, also known as a contingency table or cross-tabulation, is a type of data display that organizes and summarizes categorical data by showing the relationship between two variables. It arranges the data into rows and columns, providing a clear visual representation of the frequencies or counts associated with the different combinations of the variables.
Variance: Variance is a statistical measure that quantifies the amount of variation or dispersion in a dataset. It represents the average squared deviation from the mean, providing a way to understand the spread or distribution of data points around the central tendency.