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PSY2628 Quantitative Research Methods and Data Analysis > Sampling, Sampling Distributions, and Normal Distributions > Flashcards

Sampling, Sampling Distributions, and Normal Distributions Flashcards

1
Q

What is inferential statistics?

A

A statistical method used to make generalizations about a population based on a sample.

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2
Q

What is a population in research?

A

The total group of individuals or items that researchers are interested in studying.

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3
Q

What is a sample in research?

A

A subset of the population selected for analysis.

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4
Q

What is a parameter in statistics?

A

A numerical value that describes a characteristic of a population (e.g., population mean).

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5
Q

What is a statistic in statistics?

A

A numerical value that describes a characteristic of a sample (e.g., sample mean).

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6
Q

What are the two main types of sampling?

A

Probability sampling and non-probability sampling.

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7
Q

What is probability sampling?

A

A sampling technique where each member of the population has a known, non-zero probability of being selected.

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8
Q

What are the advantages of probability Sampling?

A

Ensures representativeness and minimises bias.

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9
Q

What are examples of probability sampling methods?

A

Simple random sampling
Stratified sampling
Cluster sampling
Systematic sampling

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10
Q

Simple Random Sampling

A

Equal chance for every individual

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11
Q

Stratified Sampling

A

Population is divided into strata (subgroups) based on a shared characteristic (e.g., age, gender, etc.), with samples taken proportionately.

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12
Q

Cluster Sampling

A

Population is divided into clusters (usually based on location or natural groupings). Entire clusters are randomly selected, and either all members of the selected clusters are surveyed, or a random sample is taken from within the clusters.

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13
Q

Systematic Sampling

A

Every individual is selected from a list - every Nth

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14
Q

What is non-probability sampling?

A

A sampling technique where the probability of selecting each member is not known.

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15
Q

What are examples of non-probability sampling methods?

A

Convenience sampling
Quota sampling
Snowball sampling

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16
Q

Convenience Sampling

A

Selecting the most important accessible participants.

17
Q

What is a sampling distribution?

A

The distribution of a sample statistic (e.g., mean) across many samples taken from the same population.

18
Q

Quota Sampling

A

Divides the population into specific subgroups (quotas) based on certain characteristics (e.g., age, gender, income). Participants are selected from each subgroup until the quota for that group is filled, but the selection is based on the researcher’s convenience or judgment rather than randomization.

19
Q

Snowball Sampling

A

Participant recruit others, useful in niche populations.

20
Q

What is the Central Limit Theorem (CLT)?

A

The theorem stating that as sample size increases, the sampling distribution of the sample mean approaches a normal distribution.

21
Q

What are the key implications of the Central Limit Theorem?

A

Larger samples (n > 30) yield normality even if the population is not normal.
It allows for hypothesis testing and confidence interval estimation.

22
Q

How does sample size affect sampling error?

A

Larger samples generally result in smaller sampling errors.

23
Q

What is a normal distribution?

A

A symmetrical, bell-shaped curve where most data points cluster around the mean.

24
Q

What are the characteristics of a normal distribution?

A

Unimodal, Symmetrical and bell-shaped
Mean = Median = Mode
Defined by mean (𝜇) and standard deviation (𝜎)

25
Q

What is the empirical rule for normal distributions?

A

68% of data within 1 SD of the mean
95% within 2 SDs
99.7% within 3 SDs

26
Q

What are the properties of the standard normal distribution?

A

Mean of 0
Standard deviation of 1
Symmetrical shape

27
Q

What is the significance of the normal curve in hypothesis testing?

A

It allows calculation of probabilities and critical values for decision-making.

28
Q

What is a t-distribution, and when is it used?

A

A distribution used when sample sizes are small and the population standard deviation is unknown.

29
Q

What are practical considerations in sampling?

A

Avoiding bias
Ensuring representativeness
Using appropriate sample sizes

30
Q

What are the effects of biased sampling?

A

It can lead to inaccurate and non-generalizable results.

31
Q

How do researchers determine an appropriate sample size?

A

Based on desired confidence level, margin of error, and population variability.

PSY2628 Quantitative Research Methods and Data Analysis (9 decks)

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