Lecture #1 - Flashcards
(54 cards)
1
Q
Descriptive Statistics
A
- are methods used to organize, summarize, and clearly present numerical data. They help in understanding the main features of a dataset by providing a clear overview, such as averages, ranges, and visual representations like graphs and charts.
2
Q
Inferential Statistics
A
- involve using data from a sample to make predictions or generalizations about a larger population. This helps in drawing conclusions beyond the immediate data, such as estimating trends or testing hypotheses.
3
Q
Population
A
- the entire group of all possible observations or data points that you’re interested in studying.
4
Q
Sample
A
- is a smaller, manageable set of observations selected from the population, used to make inferences or conclusions about the whole
population.
5
Q
Variable
A
- is something that can change or vary and represents characteristics or observations. It can include physical traits, attitudes, or behaviors that take on different values.
6
Q
What Are Some Examples Of Variables?
A
- Height
- Extraversion
- Intelligence
- Self-esteem
7
Q
Discrete Observations
A
- These are data points that can only take on specific, separate values, typically whole
numbers. There are no possible values between these fixed points.
8
Q
What’s An Example Of A Discrete Observation?
A
- the number of students in a class (you can’t have half a student).
9
Q
Continuous Observations
A
- These are data points that can take on any value within a range, including decimals
and fractions. There are infinitely many possible values between any two points.
10
Q
Whats An Example Of Continuous Observations?
A
- height or weight can be measured with great precision, including decimal places.
11
Q
Nominal Variables
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- hese are variables used for observations that fall into categories or names without a specific order or ranking.
12
Q
Nominal Variable Examples:
A
- Gender, Handedness, hair Colour, Nationality, Race, Religion. These categories are distinct and cannot be quantitatively compared.
13
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Ordinal variable:
A
- These are variables used for observations that involve a ranking or order, where the
positions (1st, 2nd, 3rd, etc.) indicate a sequence but do not specify the exact
difference between ranks.
14
Q
What Are Examples Of Ordinal Variables
A
- Birth Order, Sport results, movie rankings
15
Q
Interval Variable
A
- These are variables where the values are numbers, and the difference (or interval)
between any two consecutive numbers is consistent and meaningful. However,
interval variables do not have a true zero point.
16
Q
What Are Some Examples Of Interval Variables?
A
- Temperature In Celsius or Fahrenheit
- IQ Scores
17
Q
Ratio Variable
A
- These are variables that meet the criteria of interval variables (i.e., they have equal
intervals between values) but also have a meaningful zero point, which represents the
absence of the quantity being measured.
18
Q
What Are Some Examples Of Ratio Variables?
A
- Temperature in Kelvin
- Number of burpees (exercises)
- Reaction Time
- Number of vegetable servings per day
- Weight
19
Q
Scale Variable
A
- A scale variable is one that meets the criteria for either an interval variable or a ratio
variable. Essentially, it includes variables with ordered values, equal intervals
between values, and, in the case of ratio variables, a meaningful zero point.
20
Q
Examples Of Scale Variables:
A
- Temperature in celsius
- weight
21
Q
Independent Variable
A
- This is the variable that represents the treatment or factor being studied. It has at least two levels (e.g., different conditions or groups), and it is manipulated or observed to
determine its effect on the dependent variable.
22
Q
Examples Of Independent Variables
A
In an experiment testing the effect of sleep on performance, the independent variable could be the amount of sleep (e.g., 4 hours vs. 8 hours)
23
Q
Dependent Variable
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- This is the outcome variable that researchers measure to see if it is affected by changes in the independent variable. It is what the researcher expects to be influenced or caused by the independent variable.
● The dependent variable is the result or data you observe and record in the study.
24
Q
Example Of The Dependent Variable
A
- In the sleep and performance experiment, the dependent variable could be the participants’ performance scores on a test, which may change depending on the amount of sleep they get.
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Confounding Variable
- A confounding variable is an unwanted factor that is linked to both the independent and dependent variables. It creates confusion because it makes it hard to determine whether changes in the dependent variable are caused by the independent variable or by the confounding variable.
● Bad for research because it can skew results and make it difficult to draw
accurate conclusions.
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What Is An Example Of A Confounding Variable
- In an experiment studying the effect of sleep on test performance, a confounding variable could be caffeine intake, which might also affect performance and vary with sleep habits.
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Reliability
- This refers to the consistency of a measure. If a test or measurement is reliable, it will yield the same results if conducted multiple times under similar conditions. However, something can be reliable but still "off" if it consistently measures inaccurately.
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Validity
-This refers to how well a test actually measures what it is supposed to measure. A test is valid if it accurately assesses the intended concept, such as measuring intelligence when that’s the goal, rather than something unrelated.
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Hypothesis Testing:
- This is the process of determining whether the relationship between variables, as proposed in a hypothesis, is supported by the evidence gathered from data.
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Operational Definition
-This specifies the exact procedures or methods used to measure or manipulate a
variable in a study. It ensures that a variable is consistently measured or manipulated in the same way.
● It clarifies how abstract concepts (like stress or intelligence) are turned into
measurable observations.
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Operational Definition Example:
- To measure stress, you might use cortisol levels or adrenaline levels in the blood, or use a blood pressure detector as an indicator of physical stress responses.
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Correlational Studies (Correlation):
- This refers to an association or relationship between two or more variables. When two
variables are correlated, it means that changes in one variable are related to changes in
the other.
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Confounding Variables
- There could be other factors (confounders) that influence both variables, making it difficult to determine which variable is actually causing the observed relationship.
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Experiment:
- An experiment is a study in which participants are randomly assigned to different conditions or levels of one or more independent variables. This random assignment helps ensure that the groups are similar at the start, allowing researchers to isolate the effects of the independent variable(s) on the dependent variable.
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Random Assignment
- This is the process by which every participant in an experiment has an equal chance of being assigned to any of the groups or conditions being tested.
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Between Group Designs
- This is an experimental design where participants are assigned to only one level or
condition of the independent variable. In other words, each participant experiences only one specific version of the treatment or manipulation.
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An Example Of Between Group Designs:
- In an experiment testing two types of study techniques, one group of participants might use technique A, while another group uses technique B. Each participant only experiences one technique, so their performance can be
compared across the two groups.
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Within Design:
- In this experimental design, all participants experience every level or condition of the
independent variable. This allows researchers to compare the effects of the different conditions within the same group of participants.
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An Example Of Within Design:
- In an experiment testing two types of study techniques, all participants would first use technique A and then technique B (or vice versa). Their performance can be compared within the same group, as each participant
is exposed to both conditions.
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Open Science
-This is an approach to research that promotes transparency, collaboration, and the sharing of research materials, data, and statistical analyses. The goal is to allow others in the scientific community to question findings, replicate studies, and ensure the validity of results.
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Skewed Distribution
- A skewed distribution occurs when the data is not symmetric, and one tail (side of the distribution) is longer or more spread out than the other. This means that the distribution is "pulled" in one direction, either toward the higher or lower end of the data.
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1. Positively Skewed (Right Skewed):
■ The tail on the right side is longer or more stretched out.
■ Most data points are clustered toward the lower end of the distribution, but there are a few higher values pulling the mean to the right.
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Example Of Positively Skewed (Right Skewed)
- Income distribution, where most people earn a modest income, but a few people earn much higher salaries.
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2. Negatively Skewed (Left Skewed)
■ The tail on the left side is longer or more stretched out.
■ Most data points are clustered toward the higher end of the distribution, but a few lower values pull the mean to the left.
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An Example Of Negatively Skewed (Left Skewed):
Age at retirement, where most people retire around 60–70 years old, but a few retire much earlier, pulling the average age down.
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Normal Distribution Or Bell Curve
- A normal distribution is a specific type of frequency distribution that has the following characteristics:
- Bell Shaped
- Symmetric
- Unimodal
Characteristics:
○ Most of the data points cluster around the center (mean), and as you move further away from the center, the frequency of data points
decreases.
○The mean, median, and mode of a perfectly normal distribution are all equal and located at the center of the distribution.
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Histogram
- A histogram is a type of bar graph used to visually represent the distribution of a
single variable, typically scale data (continuous data). It shows how often different values (or ranges of values) occur in a dataset.
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Grouped Frequency Table
- A grouped frequency table is used when the data has many possible values. Instead of
listing every single value, it groups the data into intervals (or ranges) and reports the
frequency of scores within each interval.
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Frequency Table
- A frequency table is a way of organizing and presenting data to show how often each
value occurs in a dataset.
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Frequency Distribution
- The description refers to methods used for organizing and displaying data to show the distribution or pattern of a set of numbers.
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Raw Score
- A raw score is the original, untransformed data point that has not yet undergone any
statistical manipulation or analysis.
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HARKing (Hypothesizing After The Results Are Known)
- This refers to the practice of creating or modifying hypotheses based on the results of
a study, rather than having a hypothesis before the study begins.
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What Are The Problems With Harking?
- It can lead to biased conclusions because the
hypothesis is formed based on the data that has already been collected, rather than testing a predefined idea. This undermines the integrity of the research by making it appear as if the hypothesis was established before the results were known.
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Preregistration
- This is a recommended open science practice where researchers outline their research
design, hypotheses, and analysis plan before starting the study. By making this
information publicly available, it helps prevent issues like data manipulation or
"p-hacking" (selectively reporting results to achieve statistical significance).
