This document provides an introduction to statistics and research design. It defines key terms like descriptive statistics, inferential statistics, populations, samples, variables, hypothesis testing, reliability and validity, research designs, correlation analysis, regression analysis, and outlier analysis. Descriptive statistics involves organizing and summarizing data, while inferential statistics allows estimating characteristics of populations based on samples. Samples are subsets of populations used to make inferences. The goal of research is to accurately measure concepts in a reliable and valid way to test hypotheses and understand relationships between variables.
1. There are four main scales of measurement in social science research: nominal, ordinal, interval, and ratio scales.
2. Nominal scales classify variables into categories with no quantitative meaning or order, while ordinal scales classify variables into ordered categories where the distances between categories are unknown.
3. Interval and ratio scales both involve ordered categories with equal distances, but a ratio scale has a true absolute zero point.
4. For a measurement to be valid and reliable, it must accurately measure the intended construct, produce consistent results over time, and be practical to administer and interpret.
This document discusses variables and different scales of measurement. It defines a variable as a concept that is capable of measurement and can take on different values. Variables are measurable whereas concepts are mental images that vary between individuals. There are different types of variables depending on their role in causal relationships and how they are measured. Variables can be measured on nominal, ordinal, interval, or ratio scales, with each scale allowing for different statistical analyses and measures of central tendency and dispersion.
Chapter 2 The Science of Psychological Measurement (Alivio, Ansula).pptxHazelLansula1
Contemporary Philippine Arts from the Region is an art produced at the present period in time. In vernacular English, “modern” and “contemporary” are synonyms. Strictly speaking, the term “contemporary art” refers to art made and produced by artists living today. Today’s artists work in and respond to a global environment that is culturally diverse, technologically advancing, and multifaceted. Working in a wide range of mediums, contemporary artists often reflect and comment on modern-day society. When
Sampling-A compact study of different types of sampleAsith Paul.K
The document discusses various topics related to data collection in research methodology. It defines data collection and explains that it must be well-planned. It also discusses different types of variables like quantitative, qualitative, dependent, independent etc. and different scales of measurement. Further, it explains different data collection methods like surveys, questionnaires, interviews and focus groups. It also discusses concepts like population, sample, sampling methods and sources of data.
This document provides an overview of key concepts in biostatistics and how to use SPSS software for data analysis. It discusses learning objectives for understanding biostatistics, different types of data (nominal, ordinal, interval, ratio) and variables (independent, dependent
Selection of appropriate data analysis techniqueRajaKrishnan M
- The document discusses choosing the right statistical method for data analysis, which depends on factors like the number and measurement level of variables, the distribution of variables, the dependence/independence structure, the nature of the hypotheses, and sample size.
- It presents flowcharts for choosing a statistical method based on whether the hypothesis involves one variable (univariate), two variables (bivariate), or more than two variables (multivariate).
- For univariate data, descriptive statistics or a one-sample t-test can be used depending on whether description or inference is the goal; for bivariate data, the choice depends on the nature of the hypothesis (difference or association) and the level of measurement (parametric or nonparame
This document provides an overview of statistics and biostatistics. It defines statistics as the collection, analysis, and interpretation of quantitative data. Biostatistics refers to applying statistical methods to biological and medical problems. Descriptive statistics are used to summarize and organize data, while inferential statistics allow generalization from samples to populations. Common statistical measures include the mean, median, and mode for central tendency, and range, standard deviation, and variance for variability. Correlation analysis examines relationships between two variables. The document discusses various data types and measurement scales used in statistics. Overall, it serves as a basic introduction to key statistical concepts for research.
This variable is nominal. It classifies respondents into categories (married, widowed, divorced, etc.) without implying any rank among them. The numbers assigned to the categories (1, 2, 3, etc.) have no mathematical meaning.
1. There are four main scales of measurement in social science research: nominal, ordinal, interval, and ratio scales.
2. Nominal scales classify variables into categories with no quantitative meaning or order, while ordinal scales classify variables into ordered categories where the distances between categories are unknown.
3. Interval and ratio scales both involve ordered categories with equal distances, but a ratio scale has a true absolute zero point.
4. For a measurement to be valid and reliable, it must accurately measure the intended construct, produce consistent results over time, and be practical to administer and interpret.
This document discusses variables and different scales of measurement. It defines a variable as a concept that is capable of measurement and can take on different values. Variables are measurable whereas concepts are mental images that vary between individuals. There are different types of variables depending on their role in causal relationships and how they are measured. Variables can be measured on nominal, ordinal, interval, or ratio scales, with each scale allowing for different statistical analyses and measures of central tendency and dispersion.
Chapter 2 The Science of Psychological Measurement (Alivio, Ansula).pptxHazelLansula1
Contemporary Philippine Arts from the Region is an art produced at the present period in time. In vernacular English, “modern” and “contemporary” are synonyms. Strictly speaking, the term “contemporary art” refers to art made and produced by artists living today. Today’s artists work in and respond to a global environment that is culturally diverse, technologically advancing, and multifaceted. Working in a wide range of mediums, contemporary artists often reflect and comment on modern-day society. When
Sampling-A compact study of different types of sampleAsith Paul.K
The document discusses various topics related to data collection in research methodology. It defines data collection and explains that it must be well-planned. It also discusses different types of variables like quantitative, qualitative, dependent, independent etc. and different scales of measurement. Further, it explains different data collection methods like surveys, questionnaires, interviews and focus groups. It also discusses concepts like population, sample, sampling methods and sources of data.
This document provides an overview of key concepts in biostatistics and how to use SPSS software for data analysis. It discusses learning objectives for understanding biostatistics, different types of data (nominal, ordinal, interval, ratio) and variables (independent, dependent
Selection of appropriate data analysis techniqueRajaKrishnan M
- The document discusses choosing the right statistical method for data analysis, which depends on factors like the number and measurement level of variables, the distribution of variables, the dependence/independence structure, the nature of the hypotheses, and sample size.
- It presents flowcharts for choosing a statistical method based on whether the hypothesis involves one variable (univariate), two variables (bivariate), or more than two variables (multivariate).
- For univariate data, descriptive statistics or a one-sample t-test can be used depending on whether description or inference is the goal; for bivariate data, the choice depends on the nature of the hypothesis (difference or association) and the level of measurement (parametric or nonparame
This document provides an overview of statistics and biostatistics. It defines statistics as the collection, analysis, and interpretation of quantitative data. Biostatistics refers to applying statistical methods to biological and medical problems. Descriptive statistics are used to summarize and organize data, while inferential statistics allow generalization from samples to populations. Common statistical measures include the mean, median, and mode for central tendency, and range, standard deviation, and variance for variability. Correlation analysis examines relationships between two variables. The document discusses various data types and measurement scales used in statistics. Overall, it serves as a basic introduction to key statistical concepts for research.
This variable is nominal. It classifies respondents into categories (married, widowed, divorced, etc.) without implying any rank among them. The numbers assigned to the categories (1, 2, 3, etc.) have no mathematical meaning.
Descriptive analysis and descriptive analytics involve examining and summarizing data using techniques like charts, graphs, and narratives to identify patterns. Common visualization tools include pie charts, bar charts, histograms, and more. Tableau, Excel, and Datawrapper are popular tools that allow users to import data and generate various visualizations. Queries allow users to sort, filter, and extract specific information from large datasets using clauses like ORDER BY and WHERE. Hypothesis testing uses the null and alternative hypotheses to determine if experimental results are statistically significant or due to chance. Analysis of variance (ANOVA) specifically tests hypotheses by comparing means across independent groups.
This document discusses different types of scales used in measurement, including nominal, ordinal, interval, and ratio scales. Each scale has different properties in terms of magnitude, equal intervals between numbers, and whether it has an absolute zero point. Nominal scales only allow categorization while ratio scales have all properties, allowing calculations like ratios. The document also covers different rating techniques used to measure constructs, such as Likert scales, semantic differential scales, Thurstone scales, and Guttman scaling.
This document discusses scales of measurement and standardized testing. It covers four scales of measurement - nominal, ordinal, interval, and ratio scales - and provides examples of variables that fall under each scale. It also discusses key assumptions underlying testing and measurement, such as the idea that traits and states can be quantified, that multiple data sources should be used for important decisions, and that tests can predict non-test behaviors. The document focuses on reliability and validity as important factors for identifying good tests and assessments. It defines reliability as consistency or stability of scores and validity as the accuracy of interpretations from scores. It covers four types of reliability - test-retest, equivalent forms, internal consistency, and inter-scorer - and three types of validity evidence
This document provides an overview of key concepts in psychological statistics. It defines statistics as procedures for organizing, summarizing, and interpreting information using facts and figures. It discusses populations and samples, variables and data, parameters and statistics, descriptive and inferential statistics, sampling error, and experimental and nonexperimental methods. It also covers scales of measurement, frequency distributions, measures of central tendency and variability, and the importance of measurement in research.
This document provides an overview of survey and correlational research methods. It defines survey research as collecting data using instruments like questionnaires to answer questions about people's opinions or characteristics. The main purposes of surveys are to gather information about groups and sample populations. Correlational research determines if and how strongly two or more variables are related by calculating correlation coefficients. Relationship studies explore factors related to complex variables, while prediction studies use correlations to predict outcomes. The document outlines different survey and correlational research designs, procedures, analyses, and considerations.
April Heyward Research Methods Class Session - 8-5-2021April Heyward
This document provides an overview of key concepts in research methods for public administration, including:
1. Levels of measurement for variables, including nominal, ordinal, interval, and ratio levels. Examples are provided for each level.
2. Common research designs such as experimental, quasi-experimental, cross-sectional, and longitudinal designs.
3. Quantitative data analysis techniques including descriptive statistics, inferential statistics like ANOVA and regression, and correlation analysis. Frequency distributions, measures of central tendency and variability are covered.
4. Confidence intervals and how they are used to estimate population parameters more accurately than point estimates, by providing a probability assessment through setting a confidence level. Common confidence levels like 90%, 95%,
Measurement is the process observing and recording the observations that are collected as part of a research effort.
Process of assigning numbers to objects or observations, the level of measurement being a function of the rules under which the numbers are assigned.
“convert the basic materials of the problem to data”
measurement and scaling is an important tool of research. by following the right and suitable scale will provide an appropriate result of research.this slide show will additionally provide the statistical testing for research measurement and scale.
- Biostatistics refers to applying statistical methods to biological and medical problems. It is also called biometrics, which means biological measurement or measurement of life.
- There are two main types of statistics: descriptive statistics which organizes and summarizes data, and inferential statistics which allows conclusions to be made from the sample data.
- Data can be qualitative like gender or eye color, or quantitative which has numerical values like age, height, weight. Quantitative data can further be interval/ratio or discrete/continuous.
- Common measures of central tendency include the mean, median and mode. Measures of variability include range, standard deviation, variance and coefficient of variation.
- Correlation describes the relationship between two variables
This document provides an overview of key concepts in data management and statistics. It defines statistics as the study of collecting, organizing, and interpreting data to make inferences about populations. The main branches are descriptive statistics, which summarizes data, and inferential statistics, which generalizes from samples to populations. It also defines key terms like population, sample, parameter, statistic, variable, data, levels of measurement, and measures of central tendency and dispersion. Measures of central tendency like mean, median, and mode are used to describe the center of data, while measures of dispersion like range and standard deviation describe how spread out data are.
This document discusses different types of measurement scales used in research including nominal, ordinal, interval, and ratio scales. It explains the key properties and appropriate statistical analyses for each scale type. Nominal scales involve simple categorization while ratio scales allow for all types of mathematical comparisons. The document also outlines important aspects of measurement such as validity, reliability, practicality, and potential sources of error. Overall, it provides an overview of measurement fundamentals for research studies.
This chapter discusses variables and different types of variables. It defines a variable as something that can be measured and can take on different values. Variables are derived from concepts and indicators are used to convert concepts into measurable variables. There are several types of variables including independent and dependent variables, and variables can be classified based on their causal relationship, study design, or unit of measurement. Measurement scales include nominal, ordinal, interval, and ratio scales, with each scale building upon the previous one and allowing for different types of statistical analysis.
This document discusses different types of measurement variables. It begins by defining measurement variables as unknown attributes that can take quantitative or qualitative values. It then describes the four main types of measurement variables: nominal, ordinal, interval, and ratio. For each type, it provides examples and characteristics. Nominal variables categorize data without order, ordinal variables rank data, interval variables use equal distances on a scale, and ratio variables have a true zero point.
The document discusses different types of variables in experimental research:
- Independent variable: Factor manipulated by researcher to determine its effect
- Dependent variable: Factor observed and measured to determine effect of independent variable
- Moderator variable: Factor that modifies relationship between independent and dependent variables
- Control variable: Factors controlled by researcher to neutralize their effects
- Intervening variable: Factor that theoretically affects phenomena but cannot be directly observed
It also discusses data types, central tendency measures, data variability measures, and statistical techniques like correlation analysis, t-tests, ANOVA that are used for quantitative analysis.
This document discusses quantitative and qualitative data analysis. It defines key terms like analysis, hypothesis, descriptive statistics, inferential statistics, and parametric and nonparametric tests. It explains the steps of quantitative data analysis which include data preparation, describing the data through summary statistics, drawing inferences through inferential statistics, and interpreting the results. Common parametric tests include t-tests, ANOVA, and correlation. Common nonparametric tests include chi-square, median, Mann-Whitney, and Wilcoxon tests. The document emphasizes accurate presentation of analyzed data through narratives and tables.
Chapter 13 Data Analysis Inferential Methods and Analysis of Time SeriesInternational advisers
This document discusses inferential statistics and time series analysis. It defines inferential statistics as ways to generalize statistics from a sample to a larger population. Common inferential methods include correlation, linear regression, ANOVA, and time series analysis. Correlation measures relationships between variables while regression predicts outcomes. ANOVA compares group means. Time series analysis models trends, seasonality, and irregular patterns over time.
This document introduces key concepts in statistics. It defines variables, populations, samples, and different types of variables. It discusses different types of research studies including correlational studies, experiments, and other quasi-experimental designs. It also outlines different scales of measurement and introduces descriptive and inferential statistics for organizing, summarizing, and making inferences about data. Notation used in statistics is defined along with the order of operations.
This document provides an introduction to key concepts in statistics. It defines variables, populations, samples, and different types of variables. It also describes different types of research studies including correlational studies, experiments, and other quasi-experimental designs. Finally, it introduces important statistical concepts like descriptive statistics, inferential statistics, sampling error, notation, and order of operations.
This document provides an introduction to key concepts in statistics. It defines variables, populations, samples, and different types of variables. It also describes different types of research studies including correlational studies, experiments, and other quasi-experimental designs. Finally, it introduces important statistical concepts like descriptive statistics, inferential statistics, sampling error, notation, and order of operations.
This document provides an introduction to key concepts in statistics. It defines variables, populations, samples, and different types of variables. It also describes different types of research studies including correlational studies, experiments, and other quasi-experimental designs. Finally, it introduces important statistical concepts like descriptive statistics, inferential statistics, sampling error, notation, and order of operations.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Descriptive analysis and descriptive analytics involve examining and summarizing data using techniques like charts, graphs, and narratives to identify patterns. Common visualization tools include pie charts, bar charts, histograms, and more. Tableau, Excel, and Datawrapper are popular tools that allow users to import data and generate various visualizations. Queries allow users to sort, filter, and extract specific information from large datasets using clauses like ORDER BY and WHERE. Hypothesis testing uses the null and alternative hypotheses to determine if experimental results are statistically significant or due to chance. Analysis of variance (ANOVA) specifically tests hypotheses by comparing means across independent groups.
This document discusses different types of scales used in measurement, including nominal, ordinal, interval, and ratio scales. Each scale has different properties in terms of magnitude, equal intervals between numbers, and whether it has an absolute zero point. Nominal scales only allow categorization while ratio scales have all properties, allowing calculations like ratios. The document also covers different rating techniques used to measure constructs, such as Likert scales, semantic differential scales, Thurstone scales, and Guttman scaling.
This document discusses scales of measurement and standardized testing. It covers four scales of measurement - nominal, ordinal, interval, and ratio scales - and provides examples of variables that fall under each scale. It also discusses key assumptions underlying testing and measurement, such as the idea that traits and states can be quantified, that multiple data sources should be used for important decisions, and that tests can predict non-test behaviors. The document focuses on reliability and validity as important factors for identifying good tests and assessments. It defines reliability as consistency or stability of scores and validity as the accuracy of interpretations from scores. It covers four types of reliability - test-retest, equivalent forms, internal consistency, and inter-scorer - and three types of validity evidence
This document provides an overview of key concepts in psychological statistics. It defines statistics as procedures for organizing, summarizing, and interpreting information using facts and figures. It discusses populations and samples, variables and data, parameters and statistics, descriptive and inferential statistics, sampling error, and experimental and nonexperimental methods. It also covers scales of measurement, frequency distributions, measures of central tendency and variability, and the importance of measurement in research.
This document provides an overview of survey and correlational research methods. It defines survey research as collecting data using instruments like questionnaires to answer questions about people's opinions or characteristics. The main purposes of surveys are to gather information about groups and sample populations. Correlational research determines if and how strongly two or more variables are related by calculating correlation coefficients. Relationship studies explore factors related to complex variables, while prediction studies use correlations to predict outcomes. The document outlines different survey and correlational research designs, procedures, analyses, and considerations.
April Heyward Research Methods Class Session - 8-5-2021April Heyward
This document provides an overview of key concepts in research methods for public administration, including:
1. Levels of measurement for variables, including nominal, ordinal, interval, and ratio levels. Examples are provided for each level.
2. Common research designs such as experimental, quasi-experimental, cross-sectional, and longitudinal designs.
3. Quantitative data analysis techniques including descriptive statistics, inferential statistics like ANOVA and regression, and correlation analysis. Frequency distributions, measures of central tendency and variability are covered.
4. Confidence intervals and how they are used to estimate population parameters more accurately than point estimates, by providing a probability assessment through setting a confidence level. Common confidence levels like 90%, 95%,
Measurement is the process observing and recording the observations that are collected as part of a research effort.
Process of assigning numbers to objects or observations, the level of measurement being a function of the rules under which the numbers are assigned.
“convert the basic materials of the problem to data”
measurement and scaling is an important tool of research. by following the right and suitable scale will provide an appropriate result of research.this slide show will additionally provide the statistical testing for research measurement and scale.
- Biostatistics refers to applying statistical methods to biological and medical problems. It is also called biometrics, which means biological measurement or measurement of life.
- There are two main types of statistics: descriptive statistics which organizes and summarizes data, and inferential statistics which allows conclusions to be made from the sample data.
- Data can be qualitative like gender or eye color, or quantitative which has numerical values like age, height, weight. Quantitative data can further be interval/ratio or discrete/continuous.
- Common measures of central tendency include the mean, median and mode. Measures of variability include range, standard deviation, variance and coefficient of variation.
- Correlation describes the relationship between two variables
This document provides an overview of key concepts in data management and statistics. It defines statistics as the study of collecting, organizing, and interpreting data to make inferences about populations. The main branches are descriptive statistics, which summarizes data, and inferential statistics, which generalizes from samples to populations. It also defines key terms like population, sample, parameter, statistic, variable, data, levels of measurement, and measures of central tendency and dispersion. Measures of central tendency like mean, median, and mode are used to describe the center of data, while measures of dispersion like range and standard deviation describe how spread out data are.
This document discusses different types of measurement scales used in research including nominal, ordinal, interval, and ratio scales. It explains the key properties and appropriate statistical analyses for each scale type. Nominal scales involve simple categorization while ratio scales allow for all types of mathematical comparisons. The document also outlines important aspects of measurement such as validity, reliability, practicality, and potential sources of error. Overall, it provides an overview of measurement fundamentals for research studies.
This chapter discusses variables and different types of variables. It defines a variable as something that can be measured and can take on different values. Variables are derived from concepts and indicators are used to convert concepts into measurable variables. There are several types of variables including independent and dependent variables, and variables can be classified based on their causal relationship, study design, or unit of measurement. Measurement scales include nominal, ordinal, interval, and ratio scales, with each scale building upon the previous one and allowing for different types of statistical analysis.
This document discusses different types of measurement variables. It begins by defining measurement variables as unknown attributes that can take quantitative or qualitative values. It then describes the four main types of measurement variables: nominal, ordinal, interval, and ratio. For each type, it provides examples and characteristics. Nominal variables categorize data without order, ordinal variables rank data, interval variables use equal distances on a scale, and ratio variables have a true zero point.
The document discusses different types of variables in experimental research:
- Independent variable: Factor manipulated by researcher to determine its effect
- Dependent variable: Factor observed and measured to determine effect of independent variable
- Moderator variable: Factor that modifies relationship between independent and dependent variables
- Control variable: Factors controlled by researcher to neutralize their effects
- Intervening variable: Factor that theoretically affects phenomena but cannot be directly observed
It also discusses data types, central tendency measures, data variability measures, and statistical techniques like correlation analysis, t-tests, ANOVA that are used for quantitative analysis.
This document discusses quantitative and qualitative data analysis. It defines key terms like analysis, hypothesis, descriptive statistics, inferential statistics, and parametric and nonparametric tests. It explains the steps of quantitative data analysis which include data preparation, describing the data through summary statistics, drawing inferences through inferential statistics, and interpreting the results. Common parametric tests include t-tests, ANOVA, and correlation. Common nonparametric tests include chi-square, median, Mann-Whitney, and Wilcoxon tests. The document emphasizes accurate presentation of analyzed data through narratives and tables.
Chapter 13 Data Analysis Inferential Methods and Analysis of Time SeriesInternational advisers
This document discusses inferential statistics and time series analysis. It defines inferential statistics as ways to generalize statistics from a sample to a larger population. Common inferential methods include correlation, linear regression, ANOVA, and time series analysis. Correlation measures relationships between variables while regression predicts outcomes. ANOVA compares group means. Time series analysis models trends, seasonality, and irregular patterns over time.
This document introduces key concepts in statistics. It defines variables, populations, samples, and different types of variables. It discusses different types of research studies including correlational studies, experiments, and other quasi-experimental designs. It also outlines different scales of measurement and introduces descriptive and inferential statistics for organizing, summarizing, and making inferences about data. Notation used in statistics is defined along with the order of operations.
This document provides an introduction to key concepts in statistics. It defines variables, populations, samples, and different types of variables. It also describes different types of research studies including correlational studies, experiments, and other quasi-experimental designs. Finally, it introduces important statistical concepts like descriptive statistics, inferential statistics, sampling error, notation, and order of operations.
This document provides an introduction to key concepts in statistics. It defines variables, populations, samples, and different types of variables. It also describes different types of research studies including correlational studies, experiments, and other quasi-experimental designs. Finally, it introduces important statistical concepts like descriptive statistics, inferential statistics, sampling error, notation, and order of operations.
This document provides an introduction to key concepts in statistics. It defines variables, populations, samples, and different types of variables. It also describes different types of research studies including correlational studies, experiments, and other quasi-experimental designs. Finally, it introduces important statistical concepts like descriptive statistics, inferential statistics, sampling error, notation, and order of operations.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
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The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
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This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
2. Statistics Defined
Statistics is a branch of science that
deals with collecting, sorting, editing,
analyzing, interpreting, and storing
data, information, knowledge, etc
2
3. Two Branches of Statistics
Descriptive statistics
◦ Involves those methods involving the collection, presentation
and characterization of a set of data in order to properly
describe the various features of that set of data
◦ Organize, summarize, and communicate numerical information
Inferential statistics
◦ Involves those methods that make possible the estimation of
a characteristic of a population or the making of a decision
concerning a population based only on sample results
◦ Use representative sample data to draw conclusions about a
population
◦ The fundamental concepts of statistical inference consist of
two major areas known as parameter estimation and
hypothesis testing.
3
4. Branches of Statistics
Descriptive: M = 80.2, SD = 4.5
◦ Describes the average score on the first test
Inferential: t(45) = 4.50, p = .02, d = .52
◦ Infers that this score is higher than a normal statistics average
4
5. Samples and Populations
A population is the whole set of measurements or counts
about which we want to draw conclusion.
◦ Could be any size
A sample is a set of observations drawn from a subset of
the population of interest OR a sub set of a population, a
set of some of the measurements or counts that comprises
the population
◦ A portion of the population
Sample results are used to estimate the population
5
6. Samples and Populations
So, why would we use samples rather than test everyone?
◦ What would be more accurate?
◦ What would be more efficient?
6
7. Accuracy Vs Precision
◦Accuracy and precision are used synonymously in
everyday speech, but in statistics they are defined more
rigorously.
◦Precision is the closeness of repeated
measurements
Where as
◦Accuracy is the closeness of a measured or
computed value to its true value
7
8. Statistics = Numbers
Mostly, statistics is all about numbers.
So … how can we make these observations into numbers?
◦ Think about all the different types of things you can measure…
8
9. Hypothesis
Hypothesis is an assertion or conjecture
concerning one or more populations.
The truth or falseness of a statistical
hypothesis is never known with absolute
certainty unless the entire population is
examined
9
10. Hypothesis
◦The structure of the hypothesis testing will be
formulated with the use of the term null
hypothesis.
◦This refers to any hypothesis to be tested and is
denoted by H0.
H0: 1 = 2 = 3
◦ The rejection of H0 leads to the acceptance
of an alternate hypothesis, denoted by H1 or
HA.
H1: Not all means are equal
10
11. Variables
Variables
◦ Observations that can take on a range of values
◦ An example: Reaction time in the Stroop Task
◦ The time to say the colors compared to the time to say the
word
11
12. Sources of Data
Primary Vs. Secondary Data Sources
◦ There are many methods by which researchers can get the
required data set.
◦ Firstly, they may seek data already published by governmental
organizations (ministries, departments, agencies, etc.) or by
non-governmental organization (international research and
development organizations, regional networks, private
companies, etc.).
◦ Such sources of data are categorized as secondary data
sources.
◦ A second method of obtaining data is through designed
experiments, dubbed as primary data sources.
12
13. Types of Variables
Qualitative Variables
◦ Variables used when the characteristic under study concerns a
traits/characters that can only be classified in categories and
not numerically measured.
◦ The resulting data are called categorical data.
◦ Color, employment status and blood types are few examples.
13
14. Types of Variables
Quantitative Variables
◦ If a characteristic is measured on a numerical scale, the resulting
data consist of a set of numbers and are called measurement
data.
◦ The term ‘quantitative variable’ is used to refer to a
characteristic that is measured on numerical scale.
◦ A few examples of numerically valued variables are height, weight
and yield.
◦ The variables that can only take integers are called discrete
variables.
◦ The name discrete is drawn from the fact that the scale is made
up of distinct numbers with gaps.
◦ On the other hand, variables that can take any value in an
interval are called continuous variables.
14
15. Types of Variables
Discrete
◦ Variables that can only take on specific values
◦ Number of students
◦ Tricky part … we can assign discrete values to things we’d
normally consider words.
◦ Political party
15
17. More Classification of Variables
Discrete quantitative data are numerical responses, which arise
from a counting process, while continuous quantitative data are
numerical responses, which arise from a measuring process.
Discrete Variables
◦ Nominal: is the simplest and most elementary type of
measurement where numbers are assigned for the sole purpose of
differentiating one object from another. When numbers are used in
a nominal scale, it cannot be added them together, or it is not
possible to calculate an average, because the scale does not have
the necessary properties to do so
◦ Ordinal: implies the measurement that has the property of order.
Here one object can be differentiated from the other and the
direction of the difference can also be specified. Statements like
‘more than’ or ‘less than’ can be used because the measuring
system has the property of order
◦ ranking of data
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18. More Classification of
Variables
Continuous Variables
◦ Interval: used with numbers that are equally spaced
◦ Interval scale is known for its character to have equality of
units. There are equal distances between observation points
on the scale. This scale specifies not only the direction of the
difference, as in the ordinal scale, but also indicates the
amount of the difference as well.
◦ Ratio: has all the characteristics of interval scale plus an
absolute zero. With an absolute zero point, statements can
be made on ratios of two observations, such as ‘twice as long’
or ‘half as fast’. Most physical scales such as time, length and
weight are ratio scales.
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19. Examples of Variables
Nominal: name of cookies
Ordinal: ranking of favorite cookies
Interval: temperature of cookies
Ratio: How many cookies are left?
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20. A distinction
The previous information talks about the type of number
you have with your variable.
◦ This type leads to the type of statistical test you should use
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21. Variables
Independent Variables (IVs)
◦ Variable you manipulate or categorize
◦ For a true experiment: must be manipulated – meaning you
changed it
◦ Generally dichotomous variables (nominal) like experimental
group versus control group
◦ For quasi experiment: used naturally occurring groups, like
gender
◦ Still dichotomous, but you didn’t assign the group
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22. Variables
Independent Variables
◦ Special case: when IVs are categorical, the groups are called
levels
◦ If political party is an IV, levels could be Democrat or
Republican
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23. Variables
Dependent Variables (DVs)
◦ The outcome information, what you measured in the study to
find differences/changes based on the IV
◦ Generally, these are interval/ratio variables (t-tests, ANOVA,
regression), but you can use nominal ones too (chi-square)
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24. Variables
Confounding Variables
◦ Variables that systematically vary with the IV so that we cannot
logically determine which variable is at work
◦ Try to control or randomize them away
◦ Confounds your other measures!
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25. Reliability and Validity
A reliable measure is consistent
◦ Measure your height today and then again tomorrow
Standardized tests are supposed to be reliable
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26. Reliability and Validity
A valid measure is one that measures what it was intended
to measure
◦ A measuring tape should accurately measure height
A good variable is both reliable and valid
◦ How do we measure this?
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27. Hypothesis Testing
Process of drawing conclusions about whether a
relationship between variables is supported or not
supported by the evidence
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28. Types of Research Designs
Experiments: studies in which participants are randomly
assigned to a condition or level of one or more independent
variables
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30. One Goal, Two Strategies
Between-groups designs
◦ Different people complete the tasks, and comparisons are
made between groups
Within-groups designs
◦ The same participants do things more than once, and
comparisons are made over time
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31. Other Research Designs
Not all research can be done through experimentation
◦ Unethical or impractical to randomly assign participants to
conditions
Correlational studies do not manipulate either variable
◦ Variables are assessed as they exist
◦ Cannot determine causality
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32. Correlation Analysis
Correlation analysis attempts to measure the strength of
relationships between two variables by means of a single number
called a correlation coefficient.
It is important to understand the physical interpretation of this
correlation coefficient and the distinction between correlation
and regression.
Correlation coefficients close to +1 or –1 indicate a close fit to a
straight line (strong correlation) and values closer to zero indicate
a very poor fit to a straight line or no correlation.
There is no convention as to what values of correlation should be
described as strong or weak. The negative correlation values tell
that the values of one variable tend to get larger as the values of
the variable get smaller and vice versa.
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33. Regression Analysis
Regression is similar to correlation in that testing for a linear
relationship between two types of measurements is made on the same
individuals.
However, regression goes further in that we can also produce an
equation describing the line of best fit through the points on the graph.
Regression analysis concerns the study of the relationships between
variables with the objective of identifying, estimating and validating the
relationship.
When using regression analysis, unlike in correlation, the two variables
have different roles. Regression is used when the value of one of the
variables is considered to be dependent on the other, or at least reliably
predicted from the other.
In correlation, we take measurement on individuals at random for both
variables, but in regression we usually choose a set of fixed values for
the independent variable (the one controlling the other).
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35. Outlier Analysis
Outlier: an extreme score - very high or very low compared
to the rest of the scores
Outlier analysis: study of the factors that influence the
dependent variable
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