Glossary
Alpha Level (α)
The predetermined probability of making a Type I error, which is the threshold for statistical significance in hypothesis testing.
Example:
Setting the alpha level (α) at 0.05 means there's a 5% chance of incorrectly rejecting a true null hypothesis.
Alternative Hypothesis
A statement that contradicts the null hypothesis, proposing that there is an effect, a difference, or a relationship.
Example:
For a new drug, the alternative hypothesis would be that the drug does have an effect on blood pressure.
Bias
A systematic error in a study's design, data collection, or analysis that causes results to consistently deviate from the true value.
Example:
If a survey about screen time is only given to students in a computer science class, the results might have bias towards higher screen times.
Blocking
A technique used in experimental design to reduce variability by grouping similar experimental units together, then randomly assigning treatments within each group.
Example:
In a study comparing two fertilizers, dividing a field into sections based on soil type and then applying both fertilizers within each section is an example of blocking.
Confounding Variables
Variables that are related to both the independent and dependent variables in a study, making it difficult to determine the true cause-and-effect relationship.
Example:
In a study on coffee's effect on alertness, sleep quality could be a confounding variable if people who drink more coffee also tend to get less sleep.
Convenience Sample
A sample selected because it is easy to access or readily available, often leading to unrepresentative results and bias.
Example:
Surveying only your friends about their favorite music genre would be a convenience sample, as it's easy but likely not representative of all students.
Leading Questions
Questions phrased in a way that suggests a desired answer or influences the respondent's choice, often a source of questioning bias.
Example:
Asking, 'How much do you enjoy the fantastic new school cafeteria?' is a leading question because it implies the cafeteria is fantastic.
Measurement Error
Inaccuracies or inconsistencies in the process of collecting data, often due to faulty instruments, poor question design, or human mistakes.
Example:
A faulty scale consistently weighs people 5 pounds heavier, leading to measurement error in a study on weight loss.
Non-response Bias
A type of sampling bias that occurs when individuals chosen for the sample cannot be contacted or refuse to participate, and these non-respondents differ significantly from those who do respond.
Example:
If a survey about online privacy is sent to 1000 people, but only 100 respond, and those 100 are all highly concerned about privacy, this could lead to non-response bias.
Null Hypothesis
A statement of no effect, no difference, or no relationship, which is assumed to be true until sufficient evidence suggests otherwise.
Example:
For a new drug, the null hypothesis would be that the drug has no effect on blood pressure.
Power
The probability of correctly rejecting a false null hypothesis, indicating the test's ability to detect a true effect or difference.
Example:
A study designed with high power is more likely to find a statistically significant difference if one truly exists between two treatments.
Questioning Bias
Bias introduced into survey results due to the way questions are phrased or the manner in which they are asked, influencing respondent answers.
Example:
A survey asking, 'Don't you agree that our school needs more funding?' exhibits questioning bias because it leads the respondent.
Random Sampling Methods
Techniques, such as simple random sampling, that ensure every individual or group in the population has an equal chance of being selected for the sample, minimizing bias.
Example:
To select 50 students for a survey, assigning each student a number and using a random number generator to pick 50 numbers is an example of random sampling methods.
Response Bias
A systematic pattern of incorrect or untruthful responses in a survey, often caused by the wording of questions, the interviewer, or the respondent's desire to please.
Example:
If students feel pressured to say they enjoyed a school assembly when asked by the principal, this could lead to response bias.
Sampling Bias
Occurs when the method used to select a sample causes it to systematically differ from the population in a way that affects the study's outcome.
Example:
Conducting a survey about student opinions on school uniforms only among students in the art club would likely introduce sampling bias.
Sampling Error
The natural variability that occurs when a sample does not perfectly represent the entire population from which it was drawn.
Example:
If a survey of 100 students about school lunch preferences shows 70% like pizza, but the true school-wide preference is 60%, this difference is due to sampling error.
Type I Error
The error of rejecting a true null hypothesis, often referred to as a 'false positive'. Its probability is denoted by alpha (α).
Example:
A new drug is tested, and researchers conclude it cures a disease when, in reality, it has no effect; this is a Type I Error.
Type II Error
The error of failing to reject a false null hypothesis, often referred to as a 'false negative'.
Example:
A new cancer screening test is developed, and it fails to detect cancer in a patient who actually has it; this is a Type II Error.
Undercoverage Bias
A type of sampling bias that occurs when some members of the population are inadequately represented in the sample.
Example:
A survey conducted only via landline phones would suffer from undercoverage bias because it excludes people who only use cell phones.
Voluntary Response Bias
A type of sampling bias that occurs when individuals self-select into a sample, typically because they have strong opinions on the topic, leading to an unrepresentative sample.
Example:
An online poll asking people to vote on a controversial political issue often suffers from voluntary response bias as only those with strong feelings are likely to participate.
Volunteer Sample
A sample consisting of individuals who choose to participate in a study, often leading to bias as participants may have strong opinions or specific characteristics.
Example:
An online poll asking people to click if they support a new city park is a volunteer sample, as only those passionate enough will respond.