Creating a typical t-test report

A typical student t-test report can include tables with Group Statistics and Independent Samples Test results.

In the following sections we will build these tables using Qlik Senset-test functions applied to two independent groups of samples, Observation and Comparison. The corresponding tables for these samples would look like this:

Independent Sample Test

Loading the sample data

Do the following:

1. Create a new app with a new sheet and open that sheet.

2. Enter the following in the data load editor:

   Table1:

   crosstable LOAD recno() as ID, * inline [

   Observation|Comparison

   35|2

   40|27

   12|38

   15|31

   21|1

   14|19

   46|1

   10|34

   28|3

   48|1

   16|2

   30|3

   32|2

   48|1

   31|2

   22|1

   12|3

   39|29

   19|37

   25|2 ] (delimiter is '|');

   In this load script, recno() is included because crosstable requires three arguments. So, recno() simply provides an extra argument, in this case an ID for each row. Without it, Comparison sample values would not be loaded.

3. Click to load data.

Creating the Group Statistics table

Do the following:

1. In the data load editor, click to go to app view, and then click the sheet you created before.

   This opens the sheet view.

2. Click Edit sheet to edit the sheet.

3. From Charts, add a table, and from Fields, add the following expressions as measures:

   Example expressions

   Label	Expression
   N	Count(Value)
   Mean	Avg(Value)
   Standard Deviation	Stdev(Value)
   Standard Error Mean	Sterr(Value)

4. Add Type as a dimension to the table.

5. Click Sorting and move Type to the top of the sorting list.

Result:

A Group Statistics table for these samples would look like this:

Group statistics

Type	N	Mean	Standard Deviation	Standard Error Mean
Comparison	20	11.95	14.61245	3.2674431
Observation	20	27.15	12.507997	2.7968933

Creating the Two Independent Sample Student's T-test table

Do the following:

1. Click Edit sheet to edit the sheet.

2. Add the following expression as a dimension to the table. =ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance Assumed', 1))

3. From Charts add a table with the following expressions as measures:

   Example expressions

   Label	Expression
   conf	if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance Assumed', 1)),TTest_conf(Type, Value),TTest_conf(Type, Value, 0))
   t	if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance Assumed', 1)),TTest_t(Type, Value),TTest_t(Type, Value, 0))
   df	if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance Assumed', 1)),TTest_df(Type, Value),TTest_df(Type, Value, 0))
   Sig. (2-tailed)	if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance Assumed', 1)),TTest_sig(Type, Value),TTest_sig(Type, Value, 0))
   Mean Difference	TTest_dif(Type, Value)
   Standard Error Difference	if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance Assumed', 1)),TTest_sterr(Type, Value),TTest_sterr(Type, Value, 0))
   95% Confidence Interval of the Difference (Lower)	if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance Assumed', 1)),TTest_lower(Type, Value,(1-(95)/100)/2),TTest_lower(Type, Value,(1-(95)/100)/2, 0))
   95% Confidence Interval of the Difference (Upper)	if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance Assumed', 1)),TTest_upper(Type, Value,(1-(95)/100)/2),TTest_upper(Type, Value,(1-(95)/100)/2, 0))

   Result:

   Independent sample test

   Type	t	df	Sig. (2-tailed)	Mean Difference	Standard Error Difference	95% Confidence Interval of the Difference (Lower)	95% Confidence Interval of the Difference (Upper)
   Equal Variance not Assumed	3.534	37.116717335823	0.001	15.2	4.30101	6.48625	23.9137
   Equal Variance Assumed	3.534	38	0.001	15.2	4.30101	6.49306	23.9069