Statistics For Psychology

Norms are statistical benchmarks derived from administering a test to a large, representative sample. They allow for the comparison of an individual’s test score to that of the reference group, providing context for interpreting the score.

• For instance, a test-taker’s percentile rank or standard deviation relative to the norm group can help gauge performance.

The correct sequence for simple random sampling involves the following steps:

1. Determine Population Size (B): Define the total population under study.
2. Identify Sampling Units (D): Clearly outline and list the population's units for selection.
3. Decide on Sample Size (C): Specify how many units (n) will be randomly selected.
4. Randomly Select Cases (A): Use randomization methods, like a random number generator, to pick the sample.

The different scales of measurement are used based on the nature of the data:

• Nominal Scale (A): Used for categories without numerical ranking, such as diagnostic groups of mental disorders (II).
• Ordinal Scale (B): Represents rank ordering, such as socio-economic status levels (I).
• Interval Scale (C): Contains equal intervals between points but lacks a true zero, such as temperature on a centigrade scale (IV).
• Ratio Scale (D): Includes a true zero, allowing for meaningful comparisons like weight measurement (III).

Parametric statistical tests rely on the following core assumptions:

1. Normal Distribution (A): The population from which samples are drawn should follow a normal distribution.
2. Interval/Ratio Scale (B): The data should be measured on an interval or ratio scale, ensuring numerical comparability.
3. Probability Sampling (D): The data should be randomly sampled to ensure representativeness.

Note: Having many outliers (C) is not an assumption but a violation, as it can distort the results of parametric tests.