A Business Encyclopedia

Applications of t-Distribution

The t-distribution is a probability distribution method wherein the hypothesis of the mean of a small sample is tested, which is drawn from the systematic population whose standard deviation is unknown. It is a statistical measure used to compare the observed data with the data expected to be obtained from a specific hypothesis.

Applications of t-distribution

The following are the important applications of the t-distribution:

  1. Test of Hypothesis of the Population Mean: (Sample size ‘n’ is small). When the population is normally distributed, and the standard deviation σ’ is unknown, then “t” statistic is calculated as:
    Application of t-distribution-1Where,
    X͞ = Sample Mean
    ? = Population Mean
    n = Sample size
    S = Standard deviation of the sample calculated by applying the following formula:Application of t-distribution-2The null hypothesis is tested to check whether there is a significant difference between the X͞ and ?. If the calculated value of ‘t’ exceeds the table value of ‘t’ at a specific significance level, then the null hypothesis is rejected considering the difference between the X͞ and ? as significant.On the other hand, if the calculated value of ‘t’ is less than the table value of ‘t’, then the null hypothesis is accepted. It is to be noted that this test is based on the degrees of freedom, i.e. n-1.
  2. Test of Hypothesis of the Difference Between Two Means: In Testing hypothesis about the difference between two means drawn from the two systematic population whose variance is unknown, then t-test can be calculated in two ways:

    Variances are equal: When the population variances, though unknown are taken as equal, then the t- statistic to be used is:Application of t-distribution-3Where,
    1 and X͞2 are the sample means of sample 1 of size n1 and sample 2 of size n2.

    S is the common standard deviation obtained by pooling the data from both the samples and can be calculated by applying the following formula:Application of t-distribution-4The null hypothesis is that there is no difference between two means and’ is accepted when the calculated value of ‘t’ at a specified significance level is less than the table value of ‘t’ and is rejected when the calculated value exceeds the table value.

    Variances are Unequal: When the population variances are not equal, then we use the unbiased estimators S12 and S22. In this case, the sampling has the huge variability than the population variability and statistic to be used is:

    Application of t-distribution-5Where,

    ?1 and ?2 are the two population means.

    This statistic may not strictly follow t-distributions, but however it can be approximated by t-distribution with the modified value for the degrees of freedom given by:

    Application of t-distribution-6

  3. Test of Hypothesis of the Difference Between Two Means With Dependent Samples: In several situations, it is possible that the samples are drawn from the two populations that are dependent on each other. Thus, the samples are said to be dependent, as each observation included in sample one is associated with the particular observation in the second sample. Hence, due to this property the t-test that will be used here is called the paired t-test.

    This test is applied in the situations when before and after experiments are to be compared. Usually, two methods are adopted that are related to each other. The following statistic is used when the means of both the methods applied is equal i.e. ?1 = ?2 Application of t-distribution-7This statistic follows t- distribution with (n-1) degrees of freedom, where d͞ = mean of the differences calculated as:Application of t-distribution-8
    S is the standard deviation of differences and is calculated by applying the following formula:

    Application of t-distribution-9n = Number of paired observations.

  4. Test of Hypothesis about the Coefficient of Correlation: There are three cases of testing the hypothesis about the coefficient of correlation. These are:

    Case -1: When the population coefficient of correlation is zero, i.e. ρ = 0.
    The coefficient of correlation measures the degree of relationship between the variables, and when ρ = 0, then there is no statistical relationship between the variables. To test the null hypothesis which assumes that there is no correlation between the population, it is necessary that the sample coefficient of correlation ‘r’ is known. The test statistic to be used is:

    Application of t-distribution-10Case -2: When the Population Coefficient of Correlation is equal to some other value, other than zero, i.e. ρ≠0. In this case, the test based on t-distribution will not be correct and hence the hypothesis is tested using the Fisher’s z- transformation. Here the ‘r’ is transformed into ‘z’ by:

    Application of t-distribution-11Here, loge is a natural logarithm. The common logarithm can be shifted to a natural algorithm by multiplying it by the factor 2.3026. Thus,loge X = 2.3026 log 10 X, where X is the positive integerSince, ½ X (2.3026) = 1.1513, then the following transformation formula is used:

    Application of t-distribution-12The following statistic is used to test the null hypotheses:

    Application of t-distribution-13This follows the normal distribution and the test is said to be more appropriate as long as the sample size is large.

    Case-3: When the hypothesis is tested for the difference between two Independent Correlation Coefficients: To test the hypothesis of two correlations derived from the two separate samples, then the difference of the two corresponding values of z is to be compared with the standard error of the difference. The following statistic is used:

    Application of t-distributWhere,
    Application of t-distribution-15

Leave a Reply

Your email address will not be published. Required fields are marked *


Related pages

meaning of attitude scalesfa benefitsdominating meaning in hindigalloping inflationjob classification method of job evaluationcomputerised definitioncash budgetsdefinition of diminishing marginal utility in economicsdefinition deontologyfiedler leadershipnsc savingscollective bargaining in india ppthow to calculate nnpwhat is the johari window modelupper limit of neftfixed asset turnover ratio exampledefinition of substitute goods in economicshanri fayoldiminishing marginal benefitstraddle definitionexplain fiscal deficitdefinition franchiseeduality economicsconvenience sampling definition statisticsstraddling positionexpectancy theory of motivation examplesmarginal costing approachtypes of financial marketsdefinition autocraticordinals meaningtask oriented leadership style definitiondefine corporate downsizingdefintion of autocraticholistically def7 c's of effective communicationintrapreneurshipexample of buzz marketingtravellers chequelaissez faire leadership meaningpure profit in economicsexplain law of diminishing marginal utilitywhat is straddle positionsystematicsamplingadam equity theory of motivationdefinition of scatter chartdefine seasonal unemploymentperformance appraisals definitiontypes of forex riskdivesting meaningdeterminants of consumer buying behavioursamuelson multiplier accelerator modelcyclical unemployment results fromsampling distribution of statisticrecruitment tamil meaningdefine liquidity ratioscomputerized business systemsflanker brandshow to compute margin of safetydefine restructureundergoes meaning in urduprofitability ratios typessamuelson theory of trade cycledefinition of accounting ratioslinear programing simplex methoddifference between perfect market and imperfect marketoligopoly market structure characteristicsexamples of denotation wordsgrowth strategies ansoff matrixwage push inflationgeocentric meaningsamuelson trade cycle modelconcept of classical conditioningwhat is franchising definitionjohari window theorycyclical unemployment definitionfixed variable cost definitionfrank knight risk uncertainty and profitheight meaning in telugu