STATSPRO

• Confidence Interval/Interval Estimate


  basic tool for estimating the unknown value of a population parameter
• Interval Estimator


  used to estimate a population parameter using a range of values.
• An interval estimate (i.e., confidence intervals) also helps one to not be so confident that the population value is

  exactly equal to the single point estimate.
• confidence level


  is the degree of confidence or certainty that the interval contains the true value of a parameter.
• significance level


  the probability of rejecting the null hypothesis when the null hypothesis is true.
• 90%


  commonly used in exploratory research or when a higher margin of error is acceptable
• 95%


  widely used, it strikes a balance between precision and reliability and is often considered standard in many fields
• 98%


  used when a higher level of certainty is required, such as in medical research or when dealing with critical decisions
• 99%


  used in situations where an extremely high level of precision and confidence is needed, such as in safety-critical systems or in situations with high stakes
• Critical Value


  represents the boundaries or values of z-scores, that indicates the point beyond which lies the rejection region. This region does not contain the true population parameter.
• Margin of Error


  difference between a point estimate and the actual value of the parameter at a given confidence level.
• t-distribution 


  a probability distribution that is used to estimate population parameters when the sample size is small (n < 30).
• Degree of Freedom


  the number of independent pieces of information needed to calculate something.
• The total area under a t-curve is 1 (or 100%, similar to the normal distribution).
• The t-distribution is bell-shaped and symmetric about , similar to the normal distribution.
• The mean, median, and mode of the -distribution are all equal to zero.
• The variance of the t-distribution is equal to which approaches 1 as it increases infinitely.
• The -distribution is a family of curves, each determined by a parameter known as degrees of freedom (df ).
• As the sample size n gets larger, the t-distribution gets closer to the normal distribution.
• How would you know if a t-distribution is appropriate for a certain statistical test?


  1.The sample is small () and randomly selected.

  2. The population standard deviation is unknown.

  3. The parent population, the population from which you are sampling, is essentially normal.
• For a single group of samples, the degrees of freedom (df) is equal to the sample size minus one: df = n - 1
• Level of precision


  used to estimate the true value of a population. expressed as percentage points.
• level of confidence


  also known as the risk level, is the probability that the sample obtained is the representative of the true population value.
• Degree of variability 


  It is the degree of variability that refers to the extent of variation or diversity in the characteristics of the population being studied.
• Using a census for small populations


  This strategy is appropriate when the population under study is small, typically consisting of 200 elements or less.
• Using a sample size of a similar study


  This strategy is suitable when prior research similar to the current study has been conducted.
• • Using published tables for fixed and predetermined criteria

  This strategy is applicable when predetermined criteria such as confidence level and margin of error are fixed.
• Slovin’s Formula

  can also be used in determining the number of samples of a population given a specific margin of error.
• Slovin's Formula


   n = N / (1+Ne^2)
• Cronbach's alpha


  is a statistic used to measure the internal consistency or reliability of a scale or test.
• Hypothesis


  explains certain phenomena in the real world. It is a statement that is a product of a person’s curiosity.
• Hypothesis Testing


  a process that requires a decision to confirm or reject one of the two opposing hypotheses about a population.
• Statistical Hypothesis


  a statement or prediction about the possible outcome or parameter of a study. It can be shown to be supported or not supported.
• Null Hypothesis


  the initial claim that is assumed to be true, that usually states that there is no significant difference and relationships between the variables. relation symbol: statement of equality, such

  as ≥, ≤, and =.
• Alternative Hypothesis


  the hypothesis that is contrary, complementary to the null hypothesis. It uses a relation symbol with no statement of equality, such as >, <, and ≠, and is denoted by
• The purpose and importance of the null hypothesis and alternative hypothesis

  provide an approximate description of the phenomena.
• Directional Test of Hypothesis or One-tailed Test


  a type of hypothesis test that makes use of only one side or tail of the distribution. It can either be a right-tailed or left-tailed test.
• Right-Tailed Test


  a type of directional test of hypothesis or one-tailed test that is used when an assertion is made that the parameter falls within the positive end of the distribution.
• Left-Tailed Test


  a type of directional test of hypothesis or one-tailed test that is used when an assertion is made that the parameter falls within the negative end of the distribution.
• Non-directional Test of Hypothesis or Two-tailed Test


  a type of hypothesis test that makes use of two opposite sides or tails of the distribution.